Spatial-domain low-coherence quantitative phase microscopy

ABSTRACT

Systems, methods and other embodiments associated with spatial-domain Low-coherence Quantitative Phase Microscopy (SL-QPM) are described herein. SL-QPM can detect structural alterations within cell nuclei with nanoscale sensitivity (0.9 nm) (or nuclear nano-morphology) for “nano-pathological diagnosis” of cancer. SL-QPM uses original, unmodified cytology and histology specimens prepared with standard clinical protocols and stains. SL-QPM can easily integrate in existing clinical pathology laboratories. Results quantified the spatial distribution of optical path length or refractive index in individual nuclei with nanoscale sensitivity, which could be applied to studying nuclear nano-morphology as cancer progresses. The nuclear nano-morphology derived from SL-QPM offers significant diagnostic value in clinical care and subcellular mechanistic insights for basic and translational research. Techniques that provide for depth selective investigation of nuclear and other cellular features are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of pending U.S. Provisional Patentapplication Ser. No. 61/912,995 (Atty. Dkt. No. 106852.121PRO) entitled“SPATIAL-DOMAIN LOW-COHERENCE QUANTITATIVE PHASE MICROSCOPY” and filedDec. 6, 2013, and is a continuation-in-part of pending U.S. patentapplication Ser. No. 13/695,230 (Atty. Dkt. No. 106852.26WOUS) entitled“SPATIAL-DOMAIN LOW-COHERENCE QUANTITATIVE PHASE MICROSCOPY” and filedMay 23, 2013, which is a U.S. national stage entry under 35 U.S.C. §371of international application PCT/US2011/035771 (Atty. Dkt. No. 106852.26PCT) entitled “SPATIAL-DOMAIN LOW-COHERENCE QUANTITATIVE PHASEMICROSCOPY” and filed May 9, 2011, which claims priority to U.S.Provisional Patent Application 61/332,881 (Atty. Dkt. No. 106852.26PRO/02125) entitled “MULTI-MODE MULTI-DIMENSIONAL ELASTIC LIGHTSCATTERING SPECTROSCOPIC MICROSCOPY” and filed May 10, 2010. Theentirety of the above-noted applications are incorporated by referenceherein.

NOTICE ON GOVERNMENT FUNDING

This invention was made with government support under grant # EB016657and grant # CA164433 awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

BACKGROUND

Quantitative knowledge of the structure of the cell has great importancefor biomedical application. However, conventional light microscopy doesnot provide sufficient quantitative information on light propagationwithin sub-cellular organelles of a biological cell. For example, canceris typically diagnosed on the basis of morphological changes,particularly alterations in nuclear structure, in the cell. Nucleararchitectural alterations are examined by bright-field microscopy ofcells stained with reagents such as Papanicolaou, Diff-Quik, Hematoxylinand Eosin stains; the observable changes in malignant cells includeenlarged nuclear size, irregularity of nuclear shape and more prominentnucleoli. However, subtle changes in the nuclear architecture may not beeasily detectable with conventional microscopy, especially in earlystages of cancer development, which can delay definitive diagnosis untilthe malignant features of nuclear architectures become significant.While nanoscale changes in nuclear architecture can be assessed byelectron microscopy, this approach is expensive, labor intensive, andrequires specialized tissue or cell handling that can damage the sampleand make it impractical for routine clinical use. Similarly, althoughoptical phase microscopy uses the ultra-sensitivity of lightinterference effect to detect sub-cellular changes in architecture,quantitative phase imaging microscopy suffers from speckle noise thathas significantly hampered its clinical utility. Thus, clinicallypractical techniques are needed to improve early detection of cancer,provide insight into the process of malignant transformation, and informdevelopment of new diagnostic tools and therapeutic agents.

Phase contrast microscopy and differential interference contrast (DIC)microscopy are capable of detecting subtle subcellular structuralalterations. They have been widely used to visualize transparent cellsin biological research, in which a minute alteration in the phase oroptical path length of internal cell structure, even just a few proteinor DNA molecules, can be detected through the intensity differences inthe image. Despite their ability to visualize transparent cells, thelack of quantitative phase information has become a limiting factor inmany biological applications. Due to the significant technicaladvancement, quantitative phase microscopy has recently emerged as asuperior phase microscopy technique, as it provides quantitative phasemeasurement of a biological cell with ultrasensitivity in detectingsubtle dynamic changes in the subcellular structure. Despite thesesignificant advances, its utility in clinical diagnosis of cancer isstill limited, largely due to the speckle noise, special requirement onsample preparations, and the lack of known diagnostic parameters forcancer.

SUMMARY

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects of the innovation. Thissummary is not an extensive overview of the innovation. It is notintended to identify key/critical elements of the innovation or todelineate the scope of the innovation. Its sole purpose is to presentsome concepts of the innovation in a simplified form as a prelude to themore detailed description that is presented later.

In aspects, the subject innovation can comprise a spatial-domainlow-coherence quantitative phase microscopy (SL-QPM) apparatus. Theapparatus can include a light source that produces white light, whichcan optionally be collimated (e.g., via a 4f imaging system thatcollimates the light from the light source, etc.). Additionally, theapparatus can comprise an objective lens that focuses the light on asample, wherein the sample scatters at least a portion of the lightback. The apparatus can further include a spectrograph that scans acrossbackscattered light from the sample and a camera coupled to thespectrograph, wherein the camera measures backscattered light scanned bythe spectrograph.

In other embodiments, the subject innovation can include a SL-QPMmethod. This method can include the acts of producing white light,optionally collimating the light from the light source, and focusing thelight onto a sample, wherein the sample scatters at least a portion ofthe light back. Additionally, it can include the acts of scanning acrossbackscattered light from the sample with a spectrograph and measuringthe scanned light with an image acquisition device such as a cameracoupled to the spectrograph.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the innovation are described herein inconnection with the following description and the annexed drawings.These aspects are indicative, however, of but a few of the various waysin which the principles of the innovation can be employed and thesubject innovation is intended to include all such aspects and theirequivalents. Other advantages and novel features of the innovation willbecome apparent from the following detailed description of theinnovation when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a Spatial-domain Low-coherence Quantitative PhaseMicroscopy (SL-QPM) instrument in accordance with aspects of theinnovation.

FIG. 2 illustrates an alternate embodiment of a SL-QPM system thatincorporates a transmission-mode geometry.

FIG. 3 illustrates an example schematic of a MD-ELSM system inaccordance with aspects of the innovation.

FIG. 4 illustrates two configurations for obtaining imaging data from asample.

FIG. 5 illustrates an example alternative embodiment of a spatial-domainlow-coherence quantitative phase microscopy (SL-QPM) system inaccordance with aspects of the subject innovation.

FIG. 6 illustrates a specially-prepared glass slide that can be usedwith systems and methods disclosed herein, in accordance with aspects ofthe subject innovation.

FIGS. 7A-7B illustrate an example optical path length (OPL) profile andthe depth selective nature of the source correlation function.

FIG. 8A illustrates example processing steps associated with SL-QPM.

FIG. 8B illustrates that selected optical path lengths of interest werenot affected by the absorption of the cytology stains.

FIG. 9 illustrates an example illustrative refractive index profile ofinternal structures of a biological cell.

FIG. 10 shows representative refractive index profiles modeled with aGaussian random field.

FIG. 11 illustrates the phase map of a polystyrene microsphere.

FIG. 12 shows the depth profile of a U.S. Air Force target as obtainedvia SL-QPM.

FIG. 13 illustrates the dependence of statistical amplitude parameterson the average refractive index, standard deviation of refractive indexand thickness.

FIGS. 14(A) and 14(B) show Papanicolaou-stained cytological images ofrepresentative intestinal epithelial cells from APC^(Min) mice comparedto those of wild-type mice obtained from a bright-field microscope andtheir corresponding OPL maps from the cell nuclei.

FIG. 14(C), shows the nuclear heterogeneity significantly increased inapproximately 20 to 30 randomly selected intestinal epithelial cellsfrom APC^(Min) mice compared to those from wild-type mice.

FIG. 15A illustrates representative cytological images and thecorresponding amplitude maps of cell nuclei of intestinal epithelialcells from the wild-type and the Min mice.

FIG. 15B shows statistical analysis of amplitude parameters incytologically normal-appearing cell nuclei from wild-type and Min mice.

FIG. 16 shows the refractive index maps from the cell nuclei ofhistologically normal cells from wild-type mice, APC^(Min) mice at 6weeks and 4.5 months.

FIG. 17 shows the statistical analysis of average refractive index fromcell nuclei in histologically normal-appearing cell nuclei fromwild-type mice, APC^(Min) mice at 6 weeks and 4.5 months.

FIG. 18 shows conventional cytology image of columnar gastric cardiacell and corresponding optical path length maps of the cell nucleus froma low-risk patient with Barrett's esophagus without dysplasia and ahigh-risk patient with Barrett's esophagus with high-grade dysplasia.

FIG. 19 shows statistical analysis of SL-QPM-derived nanoscale nucleararchitecture parameters (standard deviation of optical path length,entropy) of cells from gastric cardia, which were significant indiscriminating high-risk esophageal patients with high-grade dysplasiaor esophageal adenocarcinoma from low-risk esophageal patients withoutany dysplasia.

FIG. 20A shows conventional histology images and correspondingrefractive index maps of histologically non-dysplastic metaplastic cellsof Barrett's esophagus (BE) mucosa from patients without dysplasia.

FIG. 20B shows conventional histology images and correspondingrefractive index maps of histologically non-dysplastic metaplastic cellsof Barrett's esophagus (BE) mucosa from patients with high-gradedysplasia.

FIG. 21 shows the statistical analysis of nuclear refractive index ofhistologically non-dysplastic metaplastic cells of BE, which wassignificantly elevated in patients with BE high-grade dysplasia oradenocarcinoma compared to those BE without dysplasia.

FIG. 22A shows representative cytological images and corresponding OPLmaps from HT29 cells.

FIG. 22B shows representative cytological images and corresponding OPLmaps from HT29 cells with C-terminus Src kinase (CSK) knock-down.

FIG. 22C shows graphs indicating that the nuclear heterogeneity wassignificantly increased in HT29 cells with CSK knock-down cell lines incomparison with HT29 cells.

FIG. 23A presents representative cytological images and therepresentative OPL maps of benign epithelial cells and indeterminate ormalignant cells from ulcerative colitis patients.

FIG. 23B shows the scatter plot of heterogeneity parameters σ_(OPL) vs.E_(OPL) obtained for all the cells from one representative patient ineach group shown in FIG. 23A.

FIG. 23C shows the results obtained from analysis of three statisticalparameters from all cells in seven patients from the groups shown inFIG. 23A.

FIG. 24A shows the distinct OPL maps obtained from representative cellsfrom benign, indeterminate, and malignant pancreatic lesions.

FIG. 24B shows the scatter plot of three statistical parameters used tocharacterize the nuclear architecture for all cells from one patient ineach group shown in FIG. 24A.

FIG. 24C shows the statistical analysis of optical parameters that wasperformed using approximately 30-40 cells for each patient for a totalof 27 patients in the training set in the study shown in FIG. 24A.

FIG. 24D shows the statistical analysis of optical parameters that wasperformed using approximately 30-40 cells for each patient for a totalof 18 patients in the validation set in the study shown in FIG. 21A.

FIG. 25 illustrates a graph that shows a statistically significantincrease in the intra-nuclear optical path length and entropy and adecrease in intra-nuclear uniformity in pancreatic cyst patients withhigh malignant potential (HMP) when compared with those in patients withlow malignant potential (LMP).

FIG. 26 shows a representative pseudo-color nuclear refractive index mapfor each of 5 patient groups.

FIG. 27 shows the results of statistical analyses of the nuclearrefractive index properties for each of the 5 patient groups in thestudy of FIG. 26.

FIG. 28 shows the nuclear refractive index properties of thenoncancerous group in the study of FIG. 26 differ significantly fromthose for age-matched patients in the uninvolved group and from thosefor age-matched patients in the malignant group.

FIG. 29 shows receiver operating characteristic (ROC) curves calculatedby using two nuclear refractive index properties for the study of FIG.26.

FIG. 30 shows the refractive index maps from a cell nucleus from abenign patient, a cell nucleus of a histologically normal cell(uninvolved) from a patient with cholangiocarcinoma, and a cell nucleusof a malignant cell from a patient with cholangiocarcinoma.

FIG. 31 shows a graph indicating that the SL-QPM-derived nanoscalenuclear refractive index of uninvolved and malignant cells fromcholangiocarcinoma patients was significantly increased.

FIG. 32 shows the comparison of nuclear refractive index and flowcytometric analysis of cells synchronized at G₁/S and G₂/M phases.

FIG. 33 shows flow cytometric analysis of HeLa cells at 4, 8, 10 and 15hours to monitor a full cell cycle.

FIG. 34 shows statistical averages of fluorescence intensity and nuclearrefractive index at different time points during a full cell cycle.

FIG. 35 shows fifteen slides with varying amounts of hematoxylin andeosin (H&E).

FIG. 36 shows the observed refractive index plotted as a function of theabsorption coefficient, along with the regression fit.

FIG. 37 visually depicts variations in stains in Min mice and breastcancer.

FIG. 38A shows nuclear index variations in intestinal tissue of ananimal model.

FIG. 38B shows nuclear index variations in tissue from core needlebiopsies of human breast.

FIG. 39 shows results indicating that the selected optical path lengthof interest is not affected by the absorption profile of the Diff-Quikstaining solution.

FIG. 40 illustrates a reflection-mode common-path interferometry setupof experiments discussed herein based on a clinically prepared glassslide.

FIG. 41 illustrates depth-resolved SL-QPM via coherence gating at fixeddepth locations.

FIG. 42 illustrates a representative thickness profile of a samplesection measured using a Dektak profilometer.

FIG. 43 illustrates a representative axial refractive index profile ofthe scattering sample.

FIG. 44 illustrates variation in the sub-resolution change in opticalpath length as a function of variation in sample thickness.

FIG. 45 illustrates the sub-resolution in change in OPL (δp) as afunction of SNR for the same refractive index profile at fixedoptical-depth locations of 1.5 μm, 3 μm, 4.5 μm and 6 μm.

FIG. 46 illustrates the δp value at the optical depth location of 1.5 μmfor changing refractive index mismatch that occurred at the opticaldepth location of 6.5 μm, quantified as a multiple of the standarddeviation.

FIG. 47 illustrates the effect of local change in mean refractive indexof the axial refractive index profile that was generated from a GRFmodel with a fixed correlation length.

FIG. 48 illustrates the relationship between δp and the profilecorrelation length, δl.

FIG. 49 illustrates the depth-resolved sub-resolution change in OPL(δp).

FIG. 50 illustrates the depth-resolved sub-resolution change in OPL (δp)for 4 μm and 5 μm thick sections at fixed optical-depth locations withinthe tissue section of 1.5 μm and 3 μm.

FIG. 51 illustrates the temporal stability of depth-resolved δp valuesas a function of time at four fixed optical depth locations.

FIG. 52 illustrates flow cytometry of HeLa cells arrested at G₁/S-phaseand at G₂/M-phase.

. FIG. 53 illustrates an example of a measured spectral signal I(x,y,λ)from a cell nucleus at and its Fourier transform after removing the biasterm.

FIG. 54 illustrates the depth-resolved sub-resolution change in OPL (δp)at four fixed optical-depth locations within the nuclei for cells atG₁/S and G₂/M phase.

FIG. 55 illustrates the depth-resolved distribution of altered nucleardensity in cells at G₂/M phase compared to G₁/S phase.

FIG. 56 illustrates an immunoblot of acetyl-Histone H4 in asynchronousand synchronized G1-phase HeLa cells treated with vehicle or TSA for 6hours.

FIG. 57 shows the δp values, which quantify depth-resolvedsub-resolution structural change in OPL, at four fixed optical-depthlocations of 1.5 μm, 3 μm, 4.5 μm and 6 μm within the cell nucleus forthe control and TSA-treated cells.

FIG. 58 illustrates the depth-resolved changes in nuclear structuralfeature vector distribution for the TSA-treated cells when compared tocontrol cells.

DETAILED DESCRIPTION

The innovation is now described with reference to the drawings, whereinlike reference numerals are used to refer to like elements throughout.In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the subject innovation. It may be evident, however,that the innovation can be practiced without these specific details.

Alterations in nuclear structure are the hallmark diagnosticcharacteristic of cancer cells. Even with well-established nuclearappearance according to tumor type and stage, definitive diagnosis ofmalignancy, particularly early in the disease course, is oftenchallenging due to morphological similarity with certain benignconditions. Using a novel optical instrument, spatial-domainlow-coherence quantitative phase microscopy (SL-QPM), it has been foundthat increased heterogeneity of nuclear architecture is diagnosticallysignificant in cancer cells at a level more sensitive than conventionalcytopathology. The spatial distribution of optical path length in theindividual cell nuclei with nanoscale sensitivity can be quantified, andcan be applied to studying nuclear heterogeneity as cancer progresses.The nanoscale nuclear architecture derived from this simple andpractical optical instrument can offer significant diagnostic value inclinical care as well as subcellular mechanistic insights for basic andtranslational research.

The innovation, in aspects thereof, comprises apparatuses, systems andmethods for spatial-domain low-coherence microscopy. FIG. 1 illustratesan example Spatial-domain Low-coherence Quantitative Phase Microscopy(SL-QPM) instrument 100 in accordance with aspects of the innovation.The hardware design of the SL-QPM 100 can include a broadband whitelight 102 (e.g., from a Xe-arc lamp) that can optionally be collimated(e.g., by a 4 f imaging system (e.g., including lenses 104 and aperture106) such as shown in example instrument 100, other optical elements(e.g., mirrors, lenses, apertures, etc.) or combinations thereof tocollimate the light, etc.) and can be focused on the sample 108 by amirror or lens (e.g., a low-NA objective lens 110 (e.g., NA=0.4)) via abeam splitter 112. The sample can be mounted on a sample stage 114. Theresulting backscattering light can be collected and projected (e.g., bya tube lens 116 via a mirror 118, etc.) onto the slit of an imagingspectrograph 120 (e.g., from Acton Research, MA), which can be coupledwith an image acquisition device (e.g., a camera such as a chargecoupled device (CCD) camera) 122 (e.g., from Andor Technology, CT),which can be mounted on a scanning stage 124. Optionally, the system canalso include a removal mirror 126 and a camera 128 to obtain opticalimages without the use of a spectrograph. The magnification of thesystem used to obtain results described herein was about 44, althoughgreater or lesser magnifications could be obtained through variations ofthe components described above. By linearly scanning the slit of thespectrograph with a small (e.g., 10 μm) step size, the backscatteringimage can be acquired. In each scanning step, the camera or other imageacquisition device (e.g., CCD camera, etc.) can record a matrix withx-axis corresponding to the wavelength and y-axis corresponding to thespatial position, resulting in a three-dimensional intensity cubeI(x,y,k), where k represents the wavenumber. The system can include aninternal processor and memory for performing calculations andprocessing, or data can be output to an external device such as acomputer. In embodiments, the system can include a data analysiscomponent 130 that can construct the three-dimensional intensity cubebased at least in part upon data recorded by the camera (etc.) 122. Thedata analysis component 130 can also perform analytical techniquesdescribed herein, such as determining parameters based on data recordedby the camera (etc.) 122.

SL-QPM can use a common-path interferometric microscopy configuration,as shown in FIG. 1. Multiple technical characteristics of SL-QPMcontribute to its high sensitivity and clinical applicability, includingreflectance-mode, low spatial-coherence illumination from a thermallight source, and spectroscopic detection. Because of thereflectance-mode geometry, the phase matching condition can pick up thelargest possible wave vectors in the Fourier-space representation of therefractive structure of the object, which carry information about itsfinest features. Compared to conventional transmission-mode lightmicroscopy, the reflectance mode can effectively suppress the effect ofabsorption owing to the staining reagents commonly used in most clinicalcytology or histology specimens and can highlight the interferencesignals. The illumination from a thermal light source coupled with alow-NA objective can provide a low transverse spatial-coherence length(e.g., in a range from less than about 500 nm to more than about 750 nm(a_(c)˜λ₀=2NA)), which can serve as a virtual aperture to eliminate thespeckle noise and can decompose a three-dimensional complex scatteringmedium into many one-dimensional channels. White-light spectroscopicdetection can provide for the ability to quantify the phase informationof the scattering object through the spectral analysis ofwavelength-dependent interference signals. The glass substrate andtissue components of the histology slides can serve as a reference and asample, respectively, that share a common path, effectively eliminatingthe slightest external disruptions that may compromise the ultrahighsensitivity.

In other embodiments, the subject innovation can include methods ofobtaining optical data regarding samples via systems, instruments, andconfigurations described herein. This data can be analyzed and processedto determine one or more parameters that characterize the data.

FIG. 2 illustrates an alternate embodiment of a SL-QPM system 200 thatincorporates a transmission-mode geometry. Additional transmission-modecomponents can be used to perform cytology and were used in connectionwith an example embodiment of the subject innovation in experimentsdescribed herein to record conventional cytology images for comparison.These additional components can include a second objective 232, one ormore additional mirrors 234, and an additional removal mirror 236, so asto direct transmission-mode images to devices 128 or 122 in order tocapture data. As shown in FIG. 2, an SL-QPM system can be incorporatedwith the functionality of conventional optical microscope, which can beaccomplished by incorporating such an SL-QPM system into existingmicroscopes, or through novel devices providing both modes.

Alterations in nuclear structure are the hallmark diagnosticcharacteristic of cancer cells. However, even with well-establishednuclear appearance according to tumor type and stage, definitivediagnosis of malignancy, particularly early in the disease course, isoften challenging due to morphological similarity with certain benignconditions. Using a novel optical instrument described herein,spatial-domain low-coherence quantitative phase microscopy (SL-QPM),increased heterogeneity of nuclear architecture has been found to bediagnostically significant in cancer cells at a level more sensitivethan conventional cytopathology. Systems and methods discussed hereincan, in aspects, provide for diagnosis of medical conditions such ascancer, etc. via SL-QPM. Using these systems and methods, the spatialdistribution of optical path length in the individual cell nuclei can bequantified with nanoscale sensitivity, which can be applied to studyingnuclear heterogeneity as cancer progresses. The nanoscale nucleararchitecture derived from this simple and practical optical instrumentcan be employed in a variety of settings, including offering significantdiagnostic value in clinical care as well as subcellular mechanisticinsights for basic and translational research.

As explained herein, spatial-domain low-coherence quantitative phasemicroscopy (SL-QPM) can be used to examine nuclear architecture incancer cells. The low spatial-coherence length from a thermal lightsource and common-path configuration can minimize the notorious noiseartifacts in quantitative phase microscopy, producing a speckle-free,nanoscale-sensitive map of the spatial variation of optical path lengthdifferences of the sub-cellular architecture. To better aid in theunderstanding of the innovation, experiments are described furtherherein, for example, experiments wherein nuclear architecturalcharacteristics of cells from a genetically modified colorectal cancercell line were examined at different levels of cell proliferation.Increased nuclear architectural heterogeneity that parallels the higherlevel of cell proliferation was observed in two cancer cell lines. Theseexperimental results confirmed the ability of SL-QPM to differentiatebenign from malignant cells in human cytological specimens, includingthe capability of SL-QPM to identify malignancy in cells characterizedby pathologists as cytologically indeterminate. As indicated by theresults discussed herein, SL-QPM can be useful in improving cancerdiagnosis and mapping nuclear content and heterogeneity duringcarcinogenesis. With these techniques, for the first time, therefractive index of the cell nuclei on the original unmodified histologyspecimens was quantified. The results show that the average refractiveindex of the cell nucleus is significantly increased in malignant cellscompared to that of the histologically normal cells. Because thesetechniques are simple, sensitive, do not require special tissueprocessing, and can be applied to archived specimens, they can be easilydisseminated to all hospital settings. Additionally, early detectionstrategies and monitoring response to preventive and therapeuticinterventions.

Using SL-QPM, nuclear architectural heterogeneity has been found toincrease in parallel with the level of cell proliferation and techniquesassociated with the subject innovation are more sensitive than standardcytopathology at identifying as cancerous otherwise cytologicallyindeterminate abnormal cells from cancer patients. This nanoscalenuclear architectural characteristic has a wide variety of applications,including cancer diagnosis. Systems and methods of the subjectinnovation, such as SL-QPM, can provide numerous advantages oftraditional systems, including ease of implementation, ultra-sensitivityof less than a nanometer, and direct applicability to clinicaldiagnosis. Using SL-QPM, speckle-free, nanoscale optical path lengthdistribution has been obtained for the first time from the cell nucleusin original cytology specimens prepared with the standard clinicalprotocol without any special processing, such as fluorescence stainingor coverslip removal. The simplicity of this optical instrument andapplicability to clinical specimens make this technique easilytranslatable to basic research and clinical care settings.

Although, throughout the following discussion, reference is primarily toSL-QPM systems, methods, and techniques, other systems, methods, andtechniques (such as multi-mode multi-dimensional elastic lightscattering spectroscopic microscopy, etc.) described herein may be usedadditionally or instead of SL-QPM in a substantially similar manner.

In other aspects, the innovation described herein include systems andmethods associated with a new imaging and spectroscopy instrument calledmulti-mode multi-dimensional elastic light scattering spectroscopicmicroscopy (MD-ELSM). In various embodiments, these systems and methodscan collect the elastically scattered light from a scattering object(e.g., biological cell, physical objects) and can provide a new type ofquantitative imaging contrast. MD-ELSM has the capability tocharacterize a full 3D scattering wave-vector with scatteringwavelength, angle and azimuth angle mapped onto the Fourier plane, aswell as onto the image plane, down to the single cell and sub-cellularlevel. MD-ELSM can uniquely quantify quantitative phase, opticalpath-length that light travels within a scattering object, averagerefractive index and its heterogeneity. Since MD-ELSM can be developedon the frame of a research microscope, it can easily incorporate variousconventional imaging modes of phase contrast (qualitative), differentialinterference contrast and fluorescence microscopy for parallelregistration of images with different modes.

FIG. 3 illustrates an example schematic of a MD-ELSM system 300 inaccordance with aspects of the innovation. An MD-ELSM system can bebuilt upon a research microscope, such as on an automated researchmicroscope stand (e.g., from Zeiss, such as an AxioObserver Z1). Theincident-light branch of the instrument can incorporate a broadbandlight source 302 such as a Xenon lamp. Optionally, a diffuser, e.g., aholographic diffuser 304 (e.g., from Edmund Optics), etc.) can be usedto homogenize the incident beam for uniform illumination for thebackscattering mode. A variable mask 306 can be included in theincident-light aperture plane in order to enable selection of theincident wave vector and illumination numerical aperture (INA), inaddition to the conventional field plane iris, as can collimating optics308 and a beam splitter 310. The transillumination branch of theinstrument can incorporate one or more of a Tungsten or other similarlamp 312, collimating optics 314, a field-plane iris, or a high-NAcondenser 316 which can have selectable optics 318 for phase contrast(phase ring), DIC (quarter wave-plate) and forward scattering mode. Theimaging branch can switch between an image acquisition device (e.g., acamera, such as a CCD camera, etc.) 320 for direct image acquisition,and the scanned spectrographic imager (e.g., CCD imager, etc.) 322 forspectroscopic detection. An achromatic beam splitter 324 can be includedto allow for simultaneous image and spectral acquisition. By insertionof lens 326, the back pupil plane (i.e., Fourier plane) of the objectivecan be imaged onto the camera, etc. 320, and the backscattering spectracan be imaged onto the entrance slit of the spectrograph coupled withcamera, etc. 322 by additional insertion of lens 328, allowing foracquisition at camera, etc. 330. This setup can obtain backscatteringspectra and image in the spatial Fourier-transform mode where each pixelcan represent a known set of 3D wave-vectors (wavelength, angle,azimuth). With insertion of only lens 328, the conventional image can bereceived by camera, etc. 320 in parallel with Fourier-domain scatteringspectra through camera, etc. 322.

Several novel features are incorporated in an MD-ELSM system such asthat shown in the setup of FIG. 3. For example, MD-ELSM can assess themulti-dimensional elastic light-scattering spectral signals that containa full set of 3D wave-vectors (scattering intensity vs. wavelength,scattering angle, azimuth angle) in both transmitted and reflectancegeometries with sub-cellular specificity. Additionally, MD-ELSM canincorporate aperture-plane variable mask to select the illuminationangle (INA) and provide the best opportunity to identify the spectralfeatures that carry the most information about variations insub-cellular structure. FIG. 4 illustrates two configurations forobtaining imaging data from a sample. Without requiring any additionalcomponents in the system, MD-ELSM can be used as a unique lowspatial-coherence and low temporal-coherence common-path interferencemicroscopy by assuming the sample configuration at 400 and high INA.Alternatively, MD-ELSM can be used as a unique path-length spectroscopicsystem at sub-cellular level by assuming the sample configuration at 410and low INA. Further, MD-ELSM has the capability to image the back pupil(Fourier plane) of the objective into the spectrographic imaging systemto obtain spectra as a function of scattering wave vector, thescattering angles, wavelengths and azimuth angles, within the numericalaperture cone of the objective. Also, it can incorporate traditionalZernike phase contrast, Nomarski differential-interference contrast, anddarkfield optical systems that also allow direct imaging and spectralanalysis of single sub-cellular organelles in living and fixed cells. Inaddition, MD-ELSM can include integrated fluorescence microscopy forfunctional and molecular imaging and spectroscopic analysis. As anotherexample, algorithms described herein can be used to characterize variousphysical properties and heterogeneity of refractive index variation.

FIG. 5 illustrates an example alternative embodiment of a spatial-domainlow-coherence quantitative phase microscopy (SL-QPM) system 500 inaccordance with aspects of the subject innovation. SL-QPM system 500includes several differences when compared with system 300, some or allof which can be included in various embodiments. The example embodimentof SL-QPM system 500 can provide a larger field-of-view, a more uniformillumination and a faster data acquisition speed when compared withsystem 300. In contrast to system 300, system 500 can employ a Köhlerillumination configuration to ensure a more uniform illumination.Additionally, system 500 can also employ an acousto-optical tunablefilter (AOTF) device in the illumination path that can scan thewavelength in the visible range (450-700 nm) to replace the spectrographof system 300 in the detection path, eliminating the need for mechanicalmoving component in the data acquisition process. Further, system 500can employ a camera (e.g., a CCD camera, a scientific complementarymetal-oxide-semiconductor (sCMOS) camera, etc.) that has a large imagingsize and high sensitivity to capture a larger field-of-view.

FIG. 6 illustrates a specially-prepared glass slide that can be usedwith systems and methods disclosed herein, in accordance with aspects ofthe subject innovation. In various aspects, the subject innovation caninclude slide preparation methods disclosed herein, as well as theability of analyzing 3D nanoscale structural changes from unstainedtissue or cell sections. In various aspects, systems and methods of thesubject innovation can include a specially-prepared glass slide such asthat illustrated in FIG. 6, in which one side can be coated with areflection-enhancing coating material (e.g., 80% transmission/20%reflection, or other ratios wherein a majority of the light istransmitted) at the interface of the sample and glass slide, and theother side can be optionally coated with anti-reflection coating. Theanti-reflection coating can remove unwanted reflection from the opticalpath, and the reflection-enhancing coating can generate a stablereference wave and enhance the signal-to-noise ratio for weaklyscattered unstained samples.

The backscattered wave from the sample is superimposed with thereference wave reflected at the glass-sample interface, resulting in aninterference signal expressed by equation 1,

$\begin{matrix}{{P(k)} = {{S(k)}\lbrack {r_{r}^{2} + {\int_{0}^{z}{{r_{s}^{2}( z^{\prime} )}{z^{\prime}}}} + {2{\int_{0}^{z}{{r_{s}( z^{\prime} )}r_{r}{\cos ( {2\; {{kn}( z^{\prime} )}z^{\prime}} )}{z^{\prime}}}}}} \rbrack}} & (1)\end{matrix}$

where S(k) is the power spectrum of the source, r_(r) is the reflectioncoefficient of the reference wave, r_(s)(z) is the scatteringcoefficient of the sample at depth z, Z is the total sample thicknessand n(z) is the refractive index distribution along the axialz-direction. The Fourier inverse of this interference signal results inequation 2,

$\begin{matrix}{{p( z_{{op}\; l} )}2\Gamma*\lbrack {{( {R_{r} + R_{s}} ){\delta (0)}} + {2r_{r}{\mathcal{F}^{- 1}( {\int_{0}^{z}{{r_{s}( z^{\prime} )}r_{r}{\cos ( {2\; {{kn}( z^{\prime} )}z^{\prime}} )}{z^{\prime}}}} )}}} \rbrack} & (2)\end{matrix}$

(where R_(r)=r_(r) ² and R_(s)=∫₀ ^(Z)r_(s) ²(z′)dz′), which is aconvolution of the source correlation function Γ with the superpositionof the reference wave and the backscattered sample wave. Thismathematical relation suggests that the source correlation functionserves as an implicit coherent window that separates the phaseinformation at each optical depth whose resolution is limited by thecoherence length, which is discussed in further detail below. Theamplitude of the Fourier-transformed signal at any given optical depthof interest gives the optical path length (OPL) distribution along theaxial direction of the sample, while the phase at each fixed opticaldepth of interest captures the sub-resolution nanoscale change in OPL atthat location. This sub-resolution change in OPL is calculated as inequation 3,

$\begin{matrix}{{\delta \; {p( z_{{op}\; l} )}} = {\frac{\lambda_{0}}{2\pi}{\arctan ( \frac{{Im}( {p( z_{opl} )} )}{{Re}( {p( z_{opl} )} )} )}}} & (3)\end{matrix}$

where z_(opl) is the fixed optical depth location, Im and Re denote theimaginary and real parts of the complex convolution p(z_(opl)),respectively, and δp(z_(opl)) is the optical path length difference(OPD) at a specific optical depth location z_(opl), which is not limitedby the resolution of optical system and can be used to probe thenanoscale structural changes of the biological cell.

To access the 3D nanoscale structural changes, the implicit coherentgating inherent in the spectral interferometry can be used, as describedbelow. According to equation 2, the Fourier transformation of thespectral interference signal is a convolution of the source correlationfunction Γ (i.e., the power spectral density of the source) and theactual OPL profile of the scattering object. As an example, assume thatthe true OPL profile of the scattering object is the distinct stem-plotin FIG. 7A.

Due to the limited spectral bandwidth of the light source, the sourcecorrelation function, as indicated in FIG. 7A, can serve as a window forcoherent gating to restrict the detected OPD to come from theback-scattered waves within this window around the given optical-depthof interest, without being affected by the scattered waves from thoseoptical depths at one or more coherence-lengths apart. The derived OPLprofile from the SL-QPM system is illustrated in FIG. 7B, where theoriginal OPL profile from the scattering object is modified by thesource correlation function. If multiple fixed optical depth locationsare chosen such that the distance between them is at least one coherencelength apart (indicated by the dots in FIG. 7A), the internal structuralchanges within the coherence-gated optical section around each opticaldepth can be captured. The physical conditions for this approach to beeffective include a closely matched refractive index between the sampleand mounting medium and that the signal strength at the selectedlocations should be above the noise floor.

The nanoscale nuclear architectural features associated with aspects ofthe present innovation can be used independently of or in conjunctionwith existing pathological diagnostic procedures or criteria. Becausethese systems and methods do not require any special sample preparation,they can be easily incorporated with existing diagnostics. Techniquesdescribed herein can enhance early detection of malignancy that wouldotherwise be missed by conventional cytopathology. As an additionalapplication, these techniques can provide a new capability forelucidating the mechanism of malignancy and correlating functional andmolecular parameters with malignancy-associated structural changes.Systems and methods of the subject innovation can thus facilitate thediscovery of new therapeutic targets and provide novel approaches tomonitoring the effects of treatment or preventive strategies. Systemsand methods of the subject innovation can be applied in a variety ofclinical settings, such as to identify the suspicious regions for canceron slides; to improve the detection of cancer, even fromnormal-appearing cells; to perform the prediction of future cancer risk;etc.

Theory and Analytical Techniques

Cancer is typically diagnosed based on the microscopic examination ofmorphological changes in the cell and tissue stained with reagents suchas hematoxylin and eosin with a conventional bright-field microscope,whose image contrast is obtained through the differences in theabsorption cross section of various stains. Due to itsdiffraction-limited resolution (˜500 nm), the observed characteristiccytologic alterations in malignant cells for cancer diagnosis are oftenlimited to overall nuclear appearance, such as enlarged nuclear size,irregularity of nuclear contour, and increased nuclear density. However,these well-established cytologic characteristics in cancer cells may notbe present or significant, especially when only a small amount of humancell or tissue samples are available for examination or in the earlycourse of the tumor development, which may delay definitive diagnosis orlead to repeat procedures to obtain additional cell and tissue samples.In contrast, aspects of the subject innovation can provide for increaseddiagnostic sensitivity.

Thanks to the significant advancement in understanding the molecularchanges of cancer cells, it is well recognized that tumorigenesis is theresult of cumulative effect of multistep genetic and epigeneticalterations. Numerous proteins, RNAs, and genetic markers that areinvolved in the carcinogenesis of malignant neoplasm have beenidentified. Those cells from cancer patients, even though classified as“indeterminate” or “normal” by pathologists, may still undergo a seriesof malignancy-associated genetic or molecular alterations. As a result,subtle structural abnormalities may occur, especially in the cellnucleus. For those structural changes at the scale of less than theresolution of conventional optical microscopy (<˜500 nm), they may notbe easily detectable by conventional pathology. However, microscopytechniques associated with the subject innovation can detect subtlepreviously pathologically undetectable cellular alterations in situ, andcan be used to improve the ability to accurately diagnose cancer. Inaspects, the innovation includes methods utilizing these techniques forearly detection of cancer.

Systems and methods associated with the subject innovation can record amicroscopic image with a reflectance intensity cube I(λ; x, y) where λis the wavelength and (x, y) represents the pixel position of the image.The phase value at every pixel can be obtained by Fourier analysis ofI(λ; x, y) and is converted to the corresponding optical path length(OPL(x, y)=

n(x,y)

L(x, y) where

n(x,y)

is the average refractive index along the axial direction (i.e.,z-direction) at the specific pixel (x, y), and L(x, y) is the physicalthickness). The sensitivity of OPL(x, y) was determined to be 0.9 nm inair. As a result, a two-dimensional, nanoscale spatial distribution ofoptical path length (OPL map) can be obtained, as shown in FIG. 8A,which illustrates processing steps that can be SL-QPM system. As shown,the spectrum from each pixel can be interpolated (e.g., with a bandwidthfrom approximately 500 nm or less to approximately 620 nm or more), andcan be windowed prior to performing fast Fourier transform. A certainfixed depth of interest can be chosen for phase processing. Thetwo-dimensional quantitative phase map can be obtained by repeatingthese steps pixel-by-pixel.

The optical path length at each pixel is mathematically the product ofrefractive index and physical thickness (OPL(x, y)=

n(x,y)

L (x,y)). Due to standardized clinical specimen preparation, mostcytology slides have a single cell layer for the same organ withconsistent physical thickness, which in the experiments discussed hereinwas confirmed by confocal microscopy measurement. Assuming the samephysical thickness, alterations in optical path length at each pixelOPL(x, y) are essentially derived from the changes in the refractiveindex, proportional to the macromolecular concentration or nucleardensity for the cell nucleus. Therefore, the changes of the nucleararchitecture in cancer cells describe the alterations in the spatialvariation of nuclear density.

The phase stability of the SL-QPM can be characterized by measuring thephase of a known sample (e.g., a US Air Force Target), and the measuredsensitivity was 0.9 nm, as obtained in a histogram of measured phaseperformed for ˜3 minutes. The final optical path length for each pixelcan be calculated by adding the optical depth of interest to its shifted(due to phase variation) path length.

The SL-QPM essentially records a microscopic image with a reflectanceinterferometric intensity cube I(x, y, k). The phase value at everypixel can be obtained by Fourier analysis of I(x, y, k) and is convertedto the corresponding OPL (OPL(x, y)=

n(x,y)

L(x,y), where

n(x,y)

is the average refractive index along the axial direction (i.e., the zdirection) at the specific pixel (x, y), and L(x, y) is the physicalthickness). As a result, a 2-D, nanoscale spatial distribution of theOPL can be obtained (e.g., as an OPL map, etc.). To quantify the imagecharacteristics of the OPL map in the nucleus, three statisticalparameters were extracted in experimental analysis discussed herein(although additional or alternative parameters could also be extracted):average OPL

OPL

over the 2-D spatial distribution of OPL(x, y); standard deviationσ_(OPL) over OPL(x, y), representing a global variation; and entropyE_(OPL), which is a measure of randomness, representing a more localizedheterogeneity derived from texture analysis. Both σ_(OPL) and E_(OPL)quantify the architectural heterogeneity.

Based on the spatial distribution of optical path length from a singlenucleus, a texture analysis can be performed, and statistical parameterscan be quantified. Three such parameters are discussed, although otherstatistical parameters can be derived in light of the innovationdisclosed herein. The average optical path length

OPL

and standard deviation σ_(OPL) can be calculated by taking the mean andstandard deviation of all OPL values from a single nucleus. The entropyE_(OPL), a parameter describing the randomness, is defined asE_(OPL)=−Σ_(i=0) ^(N-1)(I_(i))log₂p(I_(i)), where I_(i) is a randomvariable indicating intensity, p(I_(i)) is the histogram of theintensity levels in a region, and N is the number of possible intensitylevels. The intra-nuclear

OPL

describes the average optical path length or average density with asingle nucleus (with the same nuclear thickness). The intra-nuclearstandard deviation σ_(OPL) and intra-nuclear entropy E_(OPL) describethe global and local heterogeneity of optical path length or nucleardensity distribution, respectively.

In aspects, intra-nuclear architectural heterogeneity parametersdiscussed further herein—for example, σ_(OPL) and E_(OPL)—can be used todistinguish malignant cells from benign cells in various tumor types andshow a high level of statistical significance to detect subtle changesof malignancy from cytopathologically indeterminate cells. The globalnuclear heterogeneity as expressed by σ_(OPL) within a single nucleus isa robust and universal parameter in detecting subtle changes of cellproliferation in cancer cell lines and identifying malignancy in the twohuman gastrointestinal tumor types with the highest level of statisticalsignificance. The entropy E_(OPL) describing the local heterogeneity ofthe nuclear architecture is the second most effective parameter andshows strong statistical significance in cell lines and cancers, forexample, colorectal and pancreatic cancers. In many instances, the cellsfrom cancer patients also exhibit a higher level of inter-nuclearheterogeneity, evidenced by the wider spread of σ_(OPL) and E_(OPL) incell nuclei from colorectal and pancreatic cancer patients. The opticalpath length averaged over single nucleus

OPL

also indicates certain capability of discriminating two extreme groupsof frankly benign and malignant cells, in agreement withcytopathological diagnosis in colorectal and pancreatic cancers, but insome situations can be a less significant parameter for detectingcytologically indeterminate malignancy.

In aspects, the subject innovation can include various statisticalmethods for evaluating optical parameters of a sample (e.g., cells ororganelles such as cell nucleus, etc.). One approach can be based on thestatistical analysis of Fourier-transformed amplitude of lowspatiotemporal-coherence backscattering interferometric signals from aninstrument that simultaneously obtains a speckle-free backscatteringmicroscopic image in which the individual pixel contains thebackscattering spectrum, such as those of other aspects of the subjectinnovation. A theoretical model of a biological cell whose refractiveindex profile is represented by one-dimensional Gaussian random fieldmodel can be constructed. Then numerical analysis techniques can bepresented based on simulated one-dimensional interferometric signalsfrom a series model of biological cells with various known statisticalproperties of refractive index. The potential of these techniques incancer detection is demonstrated with experimental results discussedherein. These results show that the statistical properties of cellnuclear structure used were able to detect the subtle changes that areotherwise undetectable by conventional cytopathology.

If the three-dimensional refractive index distribution of a biologicalcell is described as n(x, y, z), the spatial profile of refractive indexof cell internal structures at a given pixel (x, y) within a singlebiological cell or a sub-cellular component can be described as a sum ofthe average axial refractive index n₀ and a spatially-varying componentn(z): n₀(z)=n+Δn(z), as shown in FIG. 9, which illustrates an examplerefractive index profile of internal structures of a biological cell.The thickness of the cell at a given pixel is L(x, y). The average axialrefractive index n₀ of the single cell can be determined by Fouriertransformation of backscattered interferometric signals.

To characterize the complex, inhomogeneous spatially-varying componentof refractive index distribution of cell internal structures within asingle biological cell, a stochastic model can be adopted, such as aone-dimensional Gaussian random field (GRF) model (e.g., in the analysisdiscussed herein, this was performed via the statistical softwarepackage R-project). The longitudinal refractive index can be consideredat a given pixel (x, y) within a single biological cell (n(z)) as aone-dimensional stochastic process having a Gaussian probability densityfunction. Each value of n(z) can be a Gaussian random variable with themean n₀=

n(z)

and its standard deviation (i.e., the average magnitude of refractiveindex variation)

Δn

=√{square root over (

[n(z)−n₀]²

)}. The two-point correlation function C_(n)(z) can be defined byequation 6:

C _(n)(z)=

[n(z=0)−n ₀ ][n(z)−n ₀]

  (6)

The Gaussian function can be used as the correlation model:C_(n)(z)=exp(−z²/(L_(c)/2)²), where L_(c) is the spatial correlationlength of refractive index representing the length scale over which thespatial correlation decreases to a negligible level. Therefore, a largern₀ can represent the denser internal structural components of abiological cell, higher

Δn

is associated with the increased spatial variation of the density ofintracellular material in the axial direction of the three-dimensionalcell and a longer L_(c) can correspond to a slowly-changing axialrefractive index profile due to the presence of large macromolecules.FIG. 10 shows the representative refractive index profiles modeled withGRF with a fixed n₀=1.4, and various correlation length L_(c) and thestandard deviation of refractive index

Δn

. The representative refractive index profiles n(z) modeled with GRF areshown at 1000 with fixed n₀=1.4 and

Δn

=0.02, but different correlation length L_(c), and at 1010 with fixedn₀=1.4 and L_(c)=140 nm and different average magnitude of refractiveindex variation

Δn

.

Assuming that there are some number (e.g., 5000) of pixels inside thecell which have common statistical properties of refractive index (e.g.,n₀ and

Δn

), the GRF model can generate a refractive index profile for each pixel.To emulate the configuration of the cytology specimens in theexperiment, a glass slide (refractive index n=1.52) was added as the topand bottom medium that sandwich the cell. For each GRF-modeled axialrefractive index profile, a one-dimensional multilayer dielectric slabmodel that implements Fresnel reflection was used to generate itsbackscattering spectrum. The backscattering spectrum from each pixel wasnumerically resampled to evenly spaced wavenumbers and multiplied by aHanning window before applying a fast Fourier transform. The Fouriertransformed data at the prominent peak corresponding to the optical pathlength of interest depth z were selected for amplitude processing. Aftertaking the discrete Fourier transform, a complex-valued F can beobtained, and the amplitude can be extracted by taking the absolutevalue of F(z) as shown in equation 7:

A(z)|_((x,y)) =|F(z)∥_((x,y))  (7)

The amplitude map was obtained by plotting the amplitude value at acertain depth plane for each pixel (e.g., 5000 pixels in total). Basedon the amplitude maps, statistical parameters can be determined. Threestatistical parameters were quantified in the discussion that follows(although others could be used): the average amplitude

A

over the two-dimensional spatial distribution of A(x, y); the standarddeviation of the amplitude σ_(A), and the ratio of σ_(A) to

A

, referred to as fluctuation ratio R=σ_(A)/

A

.

Using systems or methods of the subject innovation, a signal can beobtained. The detected signal as a function of wavenumber can berepresented by equation 8:

I(k)|_((x,y)) =S(k){R _(r) +R _(s)+2√{square root over (R _(r) R_(s))}cos(2k(p ₀+Δ_(p)))}|_((x,y))  (8)

where S(k) is the source power spectral density, R_(r) and R_(s)represent the reference reflectivity and sample reflectivityrespectively, p₀ corresponds to the optical path length of interest andΔp is the optical path length difference between the reference andsample beams. The first two terms in equation 8 represent the DC terms,while the third term represents those related to the interference.

To account for the effect of variation in the stain-induced nuclearrefractive index, a refractive index correction model can be used:n_(c)(x,y)=n(x,y)−Δn_(c), where n_(c) is the corrected refractive index,n is the measured refracted index before the correction, and Δn_(c) isthe refractive index correction factor accounting for the addition ofstaining agent. For a given clinical histology sample, the correctionfactor Δn_(c) can be calculated through the equation: Δn_(c)=βα, where αis the absorption coefficient of the sample, and β is the modifiedspecific refraction increment, which is a constant factor. Thiscorrection model follows directly from the linear relation betweenrefractive index and change in cell dry mass concentration. Theabsorption coefficient α of each sample can be derived from the imageobtained in the transmission mode based on Beer-Lambert's law(T=10^(−αL), where L is sample thickness). The modified specificrefraction increment β can be computed from the calibration sample setconsisting of a series of standard calibration histology slides preparedwith different amounts of H&E stain. The corrected nuclear refractiveindex map n_(c)(x, y) can be used in the analysis of samples inaccordance with aspects of the innovation.

Experimental Results

To aid in the understanding of aspects of the subject innovation,experimental results associated with specific experiments that wereconducted are discussed herein. However, although for the purposes ofobtaining the results discussed herein, specific choices were made as tothe selection of various aspects of the experiments and associatedsetups—such as the disease being studied (e.g., cancer, as opposed tonon-cancerous conditions; specific types of cancer; etc.), choice ofspecific statistical parameters, setup of optical apparatuses, etc.—thesystems and methods described herein can be employed in other contexts,as well.

In connection with the experiments discussed herein, those involvinghuman specimens were performed and all specimens were collected with theapproval of the Institutional Review Boards at University of Pittsburghand University of Washington. All animal studies were performed inaccordance with the institutional Animal Care and Use Committee ofUniversity of Pittsburgh. All mice were housed in microisolator cages ina room illuminated from 7:00 AM to 7:00 PM (12:12 hour light-darkcycle), with access to water and ad libitum.

The cells on cytology slides from human patients were typically fixedwith air-drying method; prepared by cell smearing on a glass slide; andstained by Diff-Quik (DQ) stains or Papanicolaou-stained andcoverslipped. Such methods typically minimize the single-cell thicknessvariations among different patients for the same type of cell in thesame organ.

The cells analyzed from histology slides were prepared with standardclinical protocol: formalin fixed, paraffin embedded and stained withhematoxylin and eosin, and coverslipped.

The statistical analyses discussed herein were performed based on theStudent's t-test. Two-tailed P-values were used for all analyses. Thealpha level was assumed to be 0.05. The two-tailed P-values of less than0.05 were considered as statistical significance.

Preliminary Results

In one experiment, measurements of polystyrene beads were carried out.The sample consisted of a glass coverslip (thickness, d˜170 μm) withpolystyrene beads that had been dried from suspension onto the backsurface. The phasemap of the microsphere is shown in FIG. 11. Thecolorbar represents the phase in radian. The diameter of the singlemicrosphere was experimentally determined to be 4.92 μm (with themicrosphere refractive index n=1.59), which was close to themanufacturer's specification of the mean diameter of 5.003±0.04 μm.

In another experiment, discussed in part above, the depth profile of aU.S. Air Force target was obtained via SL-QPM, as shown in FIG. 12.Lateral resolution was demonstrated by the resolving the smallest bar(spatial period of 4.4 μm). The color bar units are nanometers.

Additionally, to analyze how amplitude parameters are affected by thestatistical properties of inhomogeneous refractive index distribution ofcell internal structures, an experiment was performed involvingnumerical simulation with a series of biological cell models whoserefractive index profile n(z) was modeled by one-dimensional Gaussianrandom field (GRF), which was described by three physical quantities:the average axial refractive index n₀, the standard deviation of axialrefractive index (i.e., the average magnitude of refractive indexvariation)

Δn

and the spatial correlation length of refractive index L_(c), as definedabove. Additionally, due to the intrinsic variations of cell thicknessL, it was considered as another important variable in the numericalmodel. All values of these physical parameters were chosen to be withinthe cytologically relevant range, which was determined from experimentaldata of cytological specimens from animal models and human patients.

Based on the Fourier analysis of one-dimensional interferometricsignals, three simple amplitude parameters were derived from techniquesdescribed herein, including the average amplitude

A

, the standard deviation of the amplitude σ_(A), and the fluctuationratio R (R=σ_(A)/

A

. FIG. 13 demonstrate the dependence of these three parameters on thestatistical properties of refractive index including average axialrefractive index n₀, the average magnitude of axial refractive indexvariation

Δn

and the cell thickness L with different spatial correlation length L_(c)of the refractive index. Assuming the biological cell had a weaklyvarying refractive index system, L_(c) was chosen from a range of 100 nmto 600 nm. The top row of FIGS. 13 (1302, 1304, and 1306) shows theeffect of n₀ on these three amplitude parameters with fixed

Δn

=0.02 and thickness L=4 μm. It was evident that with increased n₀,

A

shows a significant decrease. On the other hand, σ_(A) was slightlydecreased and R was moderately increased with the increase of n₀. Themiddle row of FIGS. 10 (1308, 1310, and 1312) shows the dependence ofthe three SL-QPM-derived parameters on

Δn

with fixed n₀=1.4 and thickness L=4 μm. Increasing

Δn

led to a significant increase in both σ_(A) and R, but it only weaklyincreased

A

. The bottom row of FIGS. 10 (1314, 1316, and 1318) revealed thecontribution of cell thickness L to the three SL-QPM-derived parameterswith fixed n₀=1.4 and

Δn

=0.02. Apparently, the variation of cell thickness in the biologicallyrelevant range had very little effect on any of the three parameters.

The results from the numerical simulation showed that although thecorrelation between amplitude parameters and cell refractive indexproperties is rather complex, each of the amplitude parameter had adominating contributor under the configuration emulating the cytologyspecimens. The average amplitude

A

was predominantly affected by the average axial refractive index n₀, andincreasing n₀ will decrease

A

. The standard deviation of the amplitude σ_(A) was most affected by theaverage magnitude of axial refractive index variation

Δn

, and increasing

Δn

will increase σ_(A). The alterations in fluctuation ratio R mainly arosefrom the changes in

Δn

, with a minor contribution from n₀, and was associated with the changesof

Δn

/n₀. All three amplitude parameters were not affected by the correlationlength of refractive index L_(c) when L_(c) was larger than 200 nm. Thefact that the small changes in cell thickness did not alter theamplitude parameters was beneficial for many biomedical applications dueto the intrinsic variation of cell thickness.

Animal Models

In one experiment, cytologically normal-appearing intestinal epithelialcells from an animal model from intestinal carcinogenesis—the APC^(Min)mouse model—it was shown that despite their indistinct cytologicalfeatures, the changes in in situ nanoscale nuclear architecturalheterogeneity can be detected using SL-QPM in small intestinalepithelial cells from the 4 to 5-month-old APC^(Min) mice with tumors,compared with the wild-type mice.

Epithelial cells were obtained from visually normal mucosa of the smallintestine with a cytology brush. The cytology brush was immersed intothe Cytolyt solution (e.g., from Cytec Corporation, Boxborough, Mass.).The slides were prepared by a standard thin prep processor (e.g., fromCytec Corporation). Cells were then stained with Papanicolaou stains andsealed with a mounting medium and coverslip.

Three C57BL APC wild-type mice and three age-matched C57BL APC^(Min)mice at 4 to 5 months old were sacrificed. The small intestines wereremoved, longitudinally opened, and washed with phosphate-bufferedsaline (PBS). Cells were examined by an expert cytopathologist andnon-cancerous-looking cells were analyzed by SL-QPM. For each mouse,about 20 to 30 cells are randomly selected for SL-QPM analysis.

To explore the ability of nanoscale nuclear architecturalcharacteristics quantified by SL-QPM to identify cancer fromcytologically indeterminate cells, some experiments used awell-established animal model of colorectal carcinogenesis—the APC^(Min)mouse model. It is a genetically modified animal model that representsthe human condition of familial adenomatous polyposis syndrome in whichthe adenomatous polyposis coli (APC) gene undergoes a germ-line mutationleading to a truncation in the APC protein and spontaneous developmentof intestinal adenomas. One experiment analyzed the cytopathologicallyindistinguishable intestinal epithelial cells from three age-matchedwild-type mice and three APC^(Min) mice at 4 to 5 months with thepresence of tumors in the small intestine. FIGS. 14A and 14B showPapanicolaou-stained cytological images (in FIG. 14A) of representativeintestinal epithelial cells from the APC^(Min) mice compared to those ofthe wild-type mice obtained from a bright-field microscope and theircorresponding OPL maps (in FIG. 14B) from the cell nuclei. The colorbarshows the magnitude of the OPL in the cell nucleus in micrometers.Although the microscopic cytological images looked similar (as confirmedby an expert cytopathologist), the OPL images that characterize theintranuclear distribution of OPL exhibit distinct differences. Based onthese OPL maps, three statistical parameters from the nuclei werecalculated: the average OPL

OPL

over the 2-D spatial distribution of OPL(x,y); the standard deviation ofthe OPL σ_(OPL) representing a global variation and the entropyE_(OPL)—a measure of randomness, representing a more localizedheterogeneity. As shown in FIG. 14C, the nuclear heterogeneityquantified by σ_(OPL) and E_(OPL) were significantly increased inapproximately 20 to 30 randomly selected intestinal epithelial cellsfrom the APC^(Min) mice compared to those from the wild-type mice (pvalue<0.001). The error bar represents the standard error. However, nostatistical difference was found in the average OPL

OPL

(p value=0.4). These results suggest that the higher nucleararchitectural heterogeneity derived from its in situ nanoscale OPL mapwas associated with carcinogenesis, underlying the potential of thistechnique in accurate cancer diagnosis at the level of single cellnucleus.

For comparison with the techniques of the subject innovation, similartexture analysis was performed on conventional bright-field cytologyimages and it was found that the heterogeneity parameters (i.e.,standard deviation σ and entropy E) cannot distinguish cytologicallynormal-appearing epithelial cell nuclei from the wild-type and APC^(Min)mice (P value=0.30 and 0.29, respectively), underlining the importanceof the nanoscale OPL map. Multiple factors showed support for therelevance of increased nuclear architectural heterogeneity parameters tothe development of carcinogenesis or malignancy. First, the increasednuclear heterogeneity parameters (σ_(OPL) and E_(OPL)) were consistentlypresented in both animal model and human cytology specimens. Second, theprogressive increase of these parameters in benign cells, indeterminatecells from cancer patients and frankly malignant cells implies thecapability of SL-QPM of detecting the subtle cytologically undetectable“transitional” alterations in cell nuclei in cancer patients. Third, thenuclear heterogeneity parameters from cytologically indeterminate ornormal cells from cancer subjects resemble those from frankly malignantcells, distinct from normal cells from their benign counterparts,indicating that these cytological indeterminate cells from cancersubjects may share common biological events with the frankly malignantcells.

Additionally, experiments were conducted to determine statisticalparameters based on amplitude. FIG. 15A shows their representativePapanicolaou-stained cytological images obtained from a conventionalbright-field microscope and the corresponding amplitude maps from thecell nuclei of histologically normal-appearing intestinal epithelialcells from Min mice and those from wild-type mice (control group). Scalebars in the image indicate 5 μm. Although the microscopic cytologicalimages look similar (as confirmed by an expert cytopathologist), theamplitude maps that characterize the refractive index variation of cellinternal structures exhibit distinct differences. The three statisticalparameters from the cell nuclei were calculated from the amplitude maps:the average amplitude

A

; the standard deviation of the amplitude σ_(A) over the entire nucleus,and the fluctuation ratio R.

FIG. 15B shows the statistical analysis of these three SL-QPM-derivedamplitude parameters from all epithelial cell nuclei in six mice(approximately 20-30 cells from each mouse and three mice in eachgroup). The error bar represents the standard errors. As seen in graph1500,

A

was significantly decreased in the cell nuclei of Min mice compared withthose from wild-type mice (P=0.05) as shown in the left graph,indicating that the average axial refractive index of cell nuclei fromMin mice is higher compared to those from the wild-type mice. Graph 1510shows that σ_(A) is significantly increased from the cell nuclei of Minmice compared to those of wild-type mice with high statisticalsignificance (P<0.0001), revealing a higher axial refractive indexfluctuation

Δn

in the cell nuclei from Min mice. Similarly, 1520 shows that R was alsosubstantially increased in the epithelial cell nuclei of Min mice(P<0.0001), which reveals the higher ratio of axial refractive indexfluctuation to the average axial refractive index (

Δn

/n₀). The value R fell within the range from 0.1 to 0.2. Based on thelookup table created by the numerical simulation with a wide range ofbiologically relevant statistical properties of refractive index (n₀,

Δn

, L_(c) and L), the correlation length L_(c) was estimated to be largerthan 100 nm.

In order to demonstrate the ability of SL-QPM-derived amplitudeparameters for sensitive detection of carcinogenesis, a well-establishedanimal model of intestinal carcinogenesis—Min mouse model was studied.The intestinal epithelial cells that appeared normal to thecytopathologist were analyzed with conventional microscopy. The resultsshowed that the normal-appearing cells from Min mice resulted in adecreased

A

and increased σ_(A), when compared with those from wild-type mice. Thisresult suggests that intestinal carcinogenesis is associated withincreasing average refractive index and refractive index fluctuations inthe cell nuclei. A higher average axial refractive index n₀ may indicatethe higher density of nuclear components (e.g., chromatin), and a higheraxial refractive index fluctuation

Δn

can be associated with increased spatial variation of the concentrationof intra-nuclear solids (e.g., DNA, RNA, protein). Notably, the factthat the standard deviation of the amplitude σ_(A) (p-value<0.0001) hada much smaller p-value than the average amplitude

A

(p-value=0.05) implies that the refractive index variation may be moresensitive than the average refractive index in detecting thecarcinogenesis-associated structural changes. It is worth pointing outthat both

A

and σ_(A) could be affected by the absorption of the stains that arecommonly used in the biological cell, especially for the clinicalspecimens, as discussed further herein. Due to the well-controlledstandardized automated staining protocol, the systemic variation ofstaining is small. The fluctuation ratio R of axial refractive index canbe used as a more experimentally robust parameter to minimize the effectof stain absorption. Such quantitative statistical information about thesubtle alterations in nuclear architecture is otherwise undetectablewith conventional optical microscopy, and requires systems and methodsof the subject innovation. To confirm that these cells were trulyindistinguishable with conventional microscopy, quantitative analysiswas performed on conventional bright-field cytology images and it wasfound that the intensity parameters (i.e., average intensity andstandard deviation of intensity) cannot distinguish cytologicallynormal-appearing epithelial cell nuclei from the wild-type and Min mice(p-value=0.10 and 0.26, respectively).

In another study, a total of 12 C57BL mice (6 wild-type, 6 age-matchedMin) were studied. A segment of small intestine and colon were cut andprocessed with standard histology protocol. The nuclear refractive indexfrom small intestine was analyzed at a pre-adenomatous stage (6 weeks)and at 4.5 months when visible multiple adenomatous polyps have beendeveloped in their small intestines. FIG. 16 shows the shows therepresentative histology images and the corresponding refractive indexmaps of cell nuclei from histologically normal cells from wild-type andMin mice at 6 weeks and 4.5 months. Although all of those cells wererated as histologically normal by expert gastrointestinal pathologist,the nuclear refractive index maps reveal a progressive change. In FIG.17, as with the nuclear refractive index maps of histologically normalcells, the statistical means of average nuclear refractive index alsoshowed a progressive change that paralleled the development ofintestinal carcinogenesis. As early as week 6 when there was no visibletumor, nuclear refractive index was significantly increased (P=0.001).Such change was further elevated in normal cells from Min mice at 4.5months (P=8.2E-11). These results demonstrate the promise ofSL-QPM-derived nuclear refractive index to detect early-stagecarcinogenesis from histologically normal cells.

Barrett's Esophagus and Esophageal Cancer

Barrett's esophagus affects up to 10 million Americans. Carefulsurveillance is essential to detect esophageal adenocarcinoma (EAC).Current surveillance (i.e., Seattle protocol) is time-consuming,invasive, expensive, and not practical to perform in every affectedindividual on a regular basis. Importantly, this approach suffers fromsignificant sampling errors with the possibility of missing cancer, dueto inability to visually detect intra-mucosal carcinoma and dysplasticlesions in flat Barrett's mucosa. The analysis of normal epithelialcells obtained by endoscopic cytology brushing could potentiallysimplify the surveillance strategy. An approach based on the concept offield effect was proposed to detect high-grade dysplasia (HGD) oresophageal adenocarcinoma (EAC) through the analysis of nondysplasticintestinal metaplasia (IM), thus identifying a high risk BE populationthat warrants intensive endoscopic surveillance. These changes werestudied via SL-QPM to allow the discrimination between those high-riskpatients with EAC and dysplasia and those low-risk patients without.

In one experiment, a pilot study was performed with 60 patientsundergoing scheduled upper endoscopy. The patients were categorized intotwo groups: low-risk (20 patients; 10 Barrett's intestinal metaplasia,10 normal esophagus) and high-risk (40 patients; 14 EAC, 18 high-gradedysplasia, 8 low-grade dysplasia). The columnar epithelial cells wereobtained via brushing from gastric cardia located at ˜2 cm below thegastroesophageal junction. The cells were fixed with Cytolyt andsubsequently prepared with Thinprep processor. Optical analysis was doneby observers blinded to clinical/endoscopic data.

FIG. 18 shows conventional cytology image of columnar gastric cardiacell and corresponding optical path length maps of the cell nucleus froma low-risk patient and a high-risk patient. The nuclear optical pathlength maps were significantly different in these two cytologicallynormal-appearing columnar epithelial cells. Several SL-QPM-derivednanoscale nuclear architecture parameters (standard deviation of opticalpath length, entropy) were significant in discriminating high-risk fromlow-risk patients, as shown in FIG. 19. A prediction model was developedbased on logistic regression by combining SLQPM-derived nucleararchitecture parameters. The experiment was able to accuratelydiscriminate patients with neoplasia in 36 out of 40 patients (90%sensitivity) while correctly identifying 12 of 20 low-risk patients (60%specificity).

In another experiment, a retrospective study was performed by selectinga total of 38 BE patients who underwent Seattle protocol biopsies. Thepatients were separated into two groups: one group of 27 BE patients (11IM, 16 HGD/EAC) as a training set and another 11 BE patients as ablinded validation set. An expert pathologist reviewed the slides andmarked non-dysplastic columnar metaplastic cells, in which nuclearrefractive index properties of approximately 40-60 columnar cell nucleifrom each patient were analyzed with SL-QPM.

FIGS. 20A-B show the representative histology images and correspondingrefractive index maps of histologically non-dysplastic metaplastic cellsof BE mucosa from patients without and with dysplasia. Despite theirsimilar histological representation, their refractive index mapsexhibited a distinct difference. The statistical analysis furtherconfirmed that nuclear refractive index was significantly elevated inpatients with HGD and EAC (P<0.05), as shown in FIG. 21. This parametercan distinguish BE patients with HGD/EAC from those without dysplasia at91% sensitivity and 77% specificity. The nuclear refractive indexproperties show great promise to detect BE patients with dysplasia fromnon-dysplastic columnar metaplastic cells.

The accurate assessment of nanoscale-sensitive nuclear refractive indexproperties by SL-QPM demonstrates its feasibility for detectingdysplastic/neoplastic Barrett's esophagus from non-dysplastic IM. Thisapproach can potentially simplify BE surveillance by limiting the needfor a meticulous FIG. biopsy protocol to a subset of high-risk BEpatients.

Modified Cell Lines

Multiple experiments were conducted to explore the ability of SL-QPM toquantify the nuclear architectural characteristics in cancer cells. Inone such experiment, genetically modified cancer cells with a type ofhuman colonic adenocarcinoma cell line (HT29) were used as a modelsystem, with two variants of HT29 cells: the original HT29 cell linestransferred with empty vector and HT29 cells with a tumor suppressorgene C-terminus Src kinase (CSK) knock-down, which increases the cellproliferation. Importantly, these two cell lines did not exhibit anymorphological features that can be distinguished cytopathologically.FIG. 22A shows representative cytological images obtained frombright-field microscope and corresponding OPL maps from HT29 cells andtheir corresponding OPL maps with the same magnification. FIG. 22B showsrepresentative cytological images and corresponding OPL maps from HT29cells with C-terminus Src kinase (CSK) knock-down. Scale bars in theimage indicate 5 μm. Although the microscopic cytological images looksimilar (as confirmed by an expert cytopathologist), the OPL images thatcharacterize the intracellular distribution of optical path length weredistinctly different in these two types of HT29 cell lines. Colorbarshows the magnitude of optical path length in a cell in microns. As seenby comparing FIGS. 22A and 22B, the OPL maps from HT29 cells with CSKknock-down that have greater cell proliferation, showed significantlyelevated OPL values in the nucleus compared with the cytoplasm. Incomparison, the nuclear OPL maps showed more homogenous patterns with aslight increase in nucleus versus cytoplasm in HT29 cells.

Based on these OPL maps, three statistical parameters from the nucleiwere quantified:

OPL

, σ_(OPL) and E_(OPL), as explained in greater detail elsewhere herein.As shown in FIG. 22C, the nuclear heterogeneity quantified by σ_(OPL)and E_(OPL) were significantly increased in 50 randomly selected HT29cells with CSK knock-down cell lines compared with 50 randomly selectedcells from HT29 (p-value=0.001 and 0.04, respectively), consistent withthe increased cell proliferation of this cancer cell line. Nostatistical difference was found in the average optical path length (

OPL

) (p-value=0.8). These results indicate that the higher nucleararchitectural heterogeneity derived from the nanoscale optical pathlength map is associated with increased cell proliferation, underlyingthe potential of this technique in accurate cancer diagnosis at thenuclear level.

The results provided herein indicated that the increased heterogeneityof nuclear density (including σ_(OPL) and E_(OPL)) provide an importantdiagnostic parameter for subtle changes of malignancy. The increase innuclear heterogeneity was clearly seen in the cell line HT29 with CSKknock-down that has increased cell proliferation and may be associatedwith the higher nuclear density and large chromatin aggregates, asobserved in transmission electron microscopic images. Progressivelyincreasing nuclear heterogeneity was further shown in benign cells,indeterminate abnormal cells from cancer sites, and frankly malignantcells from cancer patients. On the other hand, the increased averagenuclear density in frankly malignant cells agreed with conventionalpathological diagnostic criteria, such as hyperchromasia. However, theelevated average nuclear density did not show a high efficacy indistinguishing cytologically indeterminate malignancy from benigncondition.

The techniques of the subject innovation should not be considered asadvanced image analysis of conventional digital microscopic images. TheOPL map provides quantitative nanoscale information about the nucleararchitecture that is otherwise undetectable with conventional opticalmicroscopy. For example, similar texture analysis was performed onconventional phase contrast microscopic images and it was found that theheterogeneity parameters (i.e., standard deviation σ and entropy E)cannot distinguish HT29 from HT29/CSK knock-down, as shown in FIGS.22A-22B, indicating the importance of the nanoscale OPL map.

Ulcerative Colitis and Colorectal Cancer

To further confirm the significance of nanoscale nuclear architecture inhuman cells and the applicability of OPL analysis in clinical cancerdiagnosis, human cytology specimens from patients with colorectal cancerand pancreatic cancer were analyzed using systems and methods discussedherein. The cytology slides were prepared with the standard clinicalprotocol, as explained in greater detail elsewhere herein. OPL analysiswas performed on those epithelial cells of interest selected by anexpert cytopathologist.

Experimental results discussed herein show that changes in thenanoscale-sensitive architectural heterogeneity of a cell nucleus can beused to differentiate benign and malignant cells in human cytologicalspecimens from patients with colorectal cancer, demonstrating thecapability of systems and methods of the subject innovation to identifymalignancy even in cells characterized by pathologists as cytologicallyindeterminate.

Banked frozen mucosal biopsies were obtained from a group of sevenpatients with ulcerative colitis who underwent colectomy or colonoscopy,four of whom had no evidence of dysplasia, and three of whom werediagnosed with colorectal cancer. Among the three cancer cases, onepatient had frankly malignant cells obtained from the cancer site, whilethe other two patients had abnormal cells from cancer sites, classifiedas indeterminate by an expert cytopathologist. The cytology slides weremade from frozen mucosal biopsies using a Touch preparation (TP) method,subsequently stained by Diff-Quik stains. These slides were reviewed byan experienced cytopathologist who dotted the colon epithelial cells ofinterest. Approximately 30-40 cell nuclei per patient were evaluated forOPL analysis.

Cytology specimens were obtained from a group of seven patients withulcerative colitis (UC), an inflammatory bowel disease, undergoingcolonoscopy or colectomy; three patients had been diagnosed withcolorectal cancer, while four had no evidence of dysplasia or cancer.The OPL maps from three groups of colon epithelial cells were analyzed:(1) normal cells (i.e., cytologically normal cells from uninvolvedtissue site (no active colitis) in the four patients without dysplasiaor cancer); (2) cytologically indeterminate abnormal cells obtained atthe cancer site (i.e., cells classified by an expert cytologist as beingindeterminate in patients with colorectal cancer); and (3) malignantcancer cells obtained at the cancer site (i.e., cytologically classifiedas malignant cells by cytologist using conventional diagnosticcriteria).

FIG. 23A presents representative cytological images and therepresentative OPL maps of benign/normal colonic epithelial cell nucleiin a patient with ulcerative colitis, abnormal cell nuclei from a cancerUC patient (colonic carcinoma) that was called indeterminate bycytological diagnosis, and abnormal malignant cell nuclei thatcytologically called malignant from a UC patient with colonic carcinoma.OPL maps reveal the significantly increased intra-nuclear heterogeneityand average optical path length in the malignant cancer cell.

To illustrate the inter-nuclear variation, FIG. 23B shows the scatterplot of heterogeneity parameters σ_(OPL), vs. E_(OPL) obtained for allthe cells from one representative patient in each group: (1) normalepithelial cells from an ulcerative colitis (no cancer) patient; (2)cytologically indeterminate abnormal cells from a cancer patient; and(3) malignant tumor cells from a cancer patient, with each cell nucleusrepresented by a point (σ_(OPL), E_(OPL)). Despite the intrinsicvariations among different nuclei, the malignant cells and normal cellswere well separated. Notably, the majority of cytologicallyindeterminate abnormal cells in cancer patient were clearlydistinguishable from normal cells from patients without dysplasia. Theseabnormal cells, classified as indeterminate by an expertcytolopathologist from a cancer site, resembled the nucleararchitectural heterogeneity of the malignant cells.

FIG. 23C shows the results obtained from analysis of three statisticalparameters from all cells in seven patients (approximately 30-40 cellsfrom each patient). The heterogeneity parameters—σ_(OPL), andE_(OPL)—were significantly increased in cytologically malignant cancercells (p-value<0.0001) compared with those in non-cancerous cells. Thesame heterogeneity parameters were also able to distinguishcytologically indeterminate abnormal cells in cancer patients from thosein patients without dysplasia with a high level of statisticalsignificance (p-value<0.0001). Although

OPL

was significantly increased in malignant cancer cells (p-value<0.01),

OPL

did not present any statistical difference in abnormal cells(cytologically indeterminate) obtained at the cancer site when comparedwith normal appearing epithelial colon cells obtained from patientswithout cancer or dysplasia. These results suggest the importance of thenanoscale nuclear heterogeneity in detecting subtle changes in tumormalignancy.

Pancreatic Studies

A definitive preoperative diagnosis of pancreatic adenocarcinoma iscritical for clinical management, particularly for the appropriate useof neoadjuvant treatment strategies. Endoscopic ultrasound-guided fineneedle aspiration (EUS-FNA) is performed on a routine basis forsuspicious pancreatic lesions, and while cytology has a relatively highspecificity (approaching 100%), the sensitivity remains sub-optimal, inpart due to the relative rarity of frankly malignant cells.

Ideally, a cytopathologist should be present on site to ensure that anaccurate diagnosis is achieved while limiting the number of needlepasses if diagnostic tissue is obtained with an early aspiration.Otherwise, 5-7 passes are needed to ensure that diagnostic material isobtained. In this latter scenario, the patients' diagnostic accuracy iscompromised by a 10-15% reduction in definitive cytologic diagnoses,longer procedure time, and increased risk for complications. Thesensitivity of EUS-guided FNA cytology for pancreatic solid lesions canbe significantly compromised by the difficulty in obtaining a diagnosticspecimen, a limited number of needle passes, and the absence of anon-site cytopathologist.

The reported diagnostic accuracy of EUS-FNA cytology varies widely, from64% to 90%. The biggest challenge in diagnosing pancreatic cancer fromEUS-FNA cytology is identifying an adequate number of cells that meetcytologic criteria of malignancy. Due to the sampling error and thepresence of a desmoplastic response, the small number of sampled cellsmay not necessarily come directly from the tumor. Furthermore, thecytopathologist looks for morphological features characteristic ofmalignant cells using a conventional bright-field light microscope,whose resolution is limited by diffraction that detects structuralalterations at the scale of ˜1 micron.

The systems and methods discussed herein, however (e.g., SL-QPM), arecapable of detecting changes in cell architecture as small as 0.9 nm,approximately 1000 times smaller than what a conventional microscopereveals. SL-QPM can use the ultra-high sensitivity of light interferenceeffect, while effectively suppressing multiple noise sources inconventional interferometer-based microscopy and accurately quantifiesthe nanoscale-sensitive optical path length distribution within eachindividual cell nucleus. Importantly, this technique can be suitable foranalyzing nanoscale architecture characteristics of cell nuclei onoriginal unmodified cytology and histology specimens prepared with thestandard clinical protocols without any additional processing orstaining.

As explained, systems and methods such as those described herein can beused for various biomedical applications, such as improving thecytological diagnostic accuracy of various cancers, and quantitativemapping of cell mass changes at sub-cellular level. Multiple examplesare described herein. For example, as described in greater detailelsewhere, the systems and methods herein can be used to quantifychanges during cell cycles, which can be applied in cancer detection.

For the first set of pancreatic results discussed herein, archivedcytology specimens obtained at University of Pittsburgh Medical Centerby endoscopic ultrasound-guided fine needle aspiration (EUS-FNA) ofsuspicious pancreatic solid lesions from 45 patients were evaluated,including 13 patients with chronic pancreatitis, and 32 patients withpancreatic malignancy (27 patients with pancreatic adenocarcinoma (PC)and 5 patients with neuroendocrine tumor (NET)). Each diagnosis had beenconfirmed by surgical pathology. Cytology had correctly identified 23 of32 patients with malignancy: 18 of 27 pancreatic adenocarcinomas (PC)and all 5 neuroendocrine tumors (NET). All cases were confirmed bysurgical pathology or at least 5-year clinical follow-up. All sampleswere prepared as air-dried smears, and then stained by Diff-Quik stains.The slides were reviewed by an experienced cytopathologist who dottedthe epithelial cells of interest on the slides. About 50 cell nuclei percase were analyzed with SL-QPM.

This cohort consisted of a training set of 27 patients and a validationset of 18 patients. The training set consisted of 27 patients: 5 werecytologically diagnosed as NET, and 13 were diagnosed as pancreaticadenocarcinomas; the remaining 9 initially received indeterminatecytological diagnosis; of these, 4 were subsequently confirmed to haveadenocarcinoma, while 5 had chronic pancreatitis. The validation setconsisted of 18 cases: 5 were cytologically diagnosed adenocarcinoma and13 were initially interpreted as indeterminate; of these, 5 weresubsequently confirmed to have pancreatic adenocarcinoma, while 8 werediagnosed with chronic pancreatitis.

The cytopathologist identified frankly malignant cells and nonmalignantappearing epithelial cells from PC patients and nonmalignant-appearingepithelial cells from benign patients for SL-QPM analysis. Sincecytology is only considered to be truly positive when a cytopathologistcan provide a definitive diagnosis of malignancy, the cytologicaldiagnoses of atypical, suspicious, and negative were all categorized as“indeterminate”. All samples were prepared as air-dried smears followedby Diff-Quik stains. The Diff-Quik method was chosen since it is thesimplest and quickest cytology preparation, most commonly used foron-site cytological diagnosis. The slides were reviewed by anexperienced cytopathologist who dotted the epithelial cells of intereston the slides for SL-QPM analysis. The individuals who performed theSL-QPM acquisition and analysis were blinded to the patient's diagnosis.The experiment analyzed approximately 30-40 cell nuclei per patient withSL-QPM. To define the efficacy and characteristics of SL-QPM-derivednanoscale-sensitive nuclear architecture, three groups of pancreaticepithelial cells were analyzed for a training set: (1) cells frompatients who were who were subsequently confirmed to have chronicpancreatitis; (2) cells labeled as “indeterminate” by the expertcytopathologist from patients who were subsequently confirmed to haveadenocarcinoma upon surgery; and (3) malignant cells from cancerpatients. FIG. 24A shows the distinct OPL maps obtained fromrepresentative cells in these three groups. The malignant cell exhibiteda more heterogeneous OPL distribution, in agreement with other findingsdiscussed herein, such as those in connection with colorectal cancercell lines and human cytology specimens.

To analyze the nucleus-to-nucleus (or cell-to-cell) variation within thesame patient, a scatter plot of these three statistical parameters forcharacterizing the nuclear architecture—

OPL

, σ_(OPL) and E_(OPL)—from all the cells in a representative patientfrom each of the three groups was obtained. All cells from patients withchronic pancreatitis and all the cytologically malignant cells are wellseparated, with almost no overlap. Notably, the distribution in thosecells from cancer patients originally labeled as “indeterminate” by theexpert cytopathologist was similar to that seen in malignant cells anddistinct from those cells from chronic pancreatitis.

To obtain a diagnostic parameter for each patient, the statisticalaverage of the three nanoscale nuclear architecture parameters (

OPL

, σ_(OPL) and E_(OPL)) was calculated over approximately 30-40 cells foreach patient, denoted as (

OPL

)_(p), (σ_(OPL))_(p) and (E_(OPL))_(p), respectively.

Statistical analysis was performed on the results from the 27 patientsin the training set, who were categorized as: (1) patients who received“indeterminate” cytological diagnosis but were surgically confirmed tohave chronic pancreatitis; (2) patients who received “indeterminate”cytological diagnosis but were subsequently found to have pancreaticadenocarcinoma; (3) patients who were cytologically diagnosed andsurgically confirmed to have a neuroendocrine tumor; and (4) patientswho were cytologically diagnosed and surgically confirmed to havepancreatic adenocarcinoma. All three nanoscale-sensitive nuclear OPLparameters (i.e., (

OPL

)_(p), (σ_(OPL))_(p) and (E_(OPL))_(p)) were significantly increased incytologically malignant cells from patients with adenocarcinoma comparedwith those from patients with chronic pancreatitis. FIG. 24B shows thescatter plot of three statistical parameters used to characterize thenuclear architecture:

OPL

, σ_(OPL) and E_(OPL) for all cells from one patient in each group. Themajority of cells from benign patients and malignant tumors were wellseparated, irrespective of their cytological diagnosis. Statisticalanalysis of all the cells from these 14 patients showed that nuclearheterogeneity expressed by σ_(OPL) and E_(OPL) were significantlyincreased in pancreatic cancer patients (p-value<0.0001), even for thosewith indeterminate cytological diagnosis, while

OPL

only showed a significant increase in cytologically malignant cells,without a high level of statistical significance in cytologicallyindeterminate cancer cells. Therefore, this nanoscale OPL analysis ofnucleus can be used to improve the diagnostic accuracy of pancreaticcancer and further demonstrates the importance of the nanoscale nuclearheterogeneity in detecting subtle changes in tumor malignancy. FIG. 24Cand FIG. 24D show the statistical analysis of E_(OPL), σ_(OPL) and

OPL

that was performed using approximately 30-40 cells for each patient fromthe patients in the training set and validation set, respectively. Thenuclear heterogeneity derived from E_(OPL), and σ_(OPL) wasprogressively increased from normal cells to cytologically indeterminateabnormal cells to frankly malignant cells. The

OPL

in malignant cells from cancer patients is significantly increasedcompared with that in normal cells (student's t-test, P<0.05). However,there was no statistically significant difference in

OPL

between normal and cytologically indeterminate abnormal cells. It shouldbe noted that for those patients who received indeterminate cytologicaldiagnosis, the nuclear heterogeneity parameters—(σ_(OPL))_(p) and(E_(OPL))_(p)—were capable of distinguishing pancreatic adenocarcinomafrom chronic pancreatitis with statistical significance (P-value<0.01).Therefore, the nanoscale nuclear architecture parameters show greatpromise to improve the diagnostic accuracy of pancreatic cancer.

A logistic regression model was developed using just the most effectiveOPL parameter—in this study, nuclear architectural randomness or entropy(E_(OPL))_(p)—with a maximal specificity (minimized false positives)from the 27 patients in the training set. This model achieved 100%specificity and 95% sensitivity.

The prediction rule from the training set was applied to another 18patients in whom 5 initially were given a correct cytological diagnosisof pancreatic adenocarcinoma and 13 received an “indeterminate”cytological diagnosis on EUS-FNA cytology specimens but weresubsequently were found to have pancreatic cancer or chronicpancreatitis upon surgical pathology. The SL-QPM-derived nucleararchitectural characteristics discriminated between patients withpancreatic adenocarcinoma and chronic pancreatitis with 90% sensitivityand 100% specificity. The nuclear entropy (E_(OPL))_(p) correctlyidentified all four pancreatic adenocarcinoma patients who received acorrect cytological diagnosis.

When combining both training and validation sets, the SL-QPM-derivednuclear entropy increased the sensitivity of cytology for identifyingpancreatic cancer from 72% to 94% while maintaining 100% specificity.Table 1 summarizes the results:

Training Validation Combined Set (%) Set (%) Set (%) CytologySensitivity 82 50 72 Specificity 100 100 100 SL-QPM Sensitivity 95 90 94Specificity 100 100 100

Compared with conventional cytology, SL-QPM can offer a novel approachof assessing the architecture properties of cell nuclei through thecharacterization of in-situ nuclear architectural properties with asensitivity of less than a nanometer, which can be extremely responsiveto even minute changes in the sub-cellular structures, well beyond whatconventional microscopy reveals. Notably, this technique can be directlyapplied to the original unmodified format (i.e., cytology slides)without any special sample preparation. Additionally, this techniquerequires only a small number of cell nuclei (˜30-40 per patient) and isthus ideally suited for the FNA cytology specimens for which a limitednumber of cells are available for analysis. Therefore, the ability toanalyze the subtle, nanoscale alterations in nuclear architecture on theoriginal cytology specimens represents a significant technicaladvancement, which allows us to target the biggest challenge in FNAcytology of pancreatic lesions: sampling error.

The nanoscale nuclear architecture properties were quantified by theproduct of refractive index and the physical thickness of the cell(OPL=nL). If assuming a monolayer of cells has consistent physicalthickness for the same cell type, the alterations in optical path lengthare essentially due to the changes in nuclear refractive index. Theincreased refractive index has previously shown to arise from theincreased mass density (i.e., macromolecular concentration). The findingof increased average optical path length (i.e. (

OPL

)_(p)) in the cell nuclei of malignant cells is likely due to theincreased density of the nuclear components, such as chromatin andnuclear matrix. Indeed, the increased nuclear density (i.e.,hyperchromasia) in cancer cells has been well documented as one of theimportant pathological criteria for cancer diagnosis.

A progressively increased nuclear heterogeneity in FNA cytologyspecimens of pancreatic solid lesions was also found in benign cells,indeterminate cells from cancer patients, and frankly malignant cellsfrom cancer patients, quantified by (σ_(OPL))_(p) and (E_(OPL))_(p). Themost important aspect of these nanoscale nuclear heterogeneityparameters is their ability to detect pancreatic cancer from those cellsconsidered as “indeterminate”.

The distribution of nuclear architectural parameters from cytologicallyindeterminate pancreatic cancer cells was found to resemble those offrankly malignant cells, distinct from cells diagnosed with benignconditions, suggesting that these cytological indeterminate cells fromcancer patients may share certain common biological characteristics withthe frankly malignant cells. Although the specific biological eventsresponsible for the increased nuclear architectural parameters in cancercells are currently not known, experiments discussed elsewhere hereinwith cancer cells synchronized at distinct cell cycle phases havedemonstrated a significant increase in nuclear density (or nuclearrefractive index) and its heterogeneity in cells arrested at G₂/M phasesin which there is increased DNA content. This result indicates thatincreased cell growth may be one possible mechanism responsible for thenuclear architectural changes detected by SL-QPM. These subtle changesin the nuclear structure could also be the results of complex geneticand molecular events from multiple molecular pathways, such as chromatinclumping, nucleolar alterations and genetic instability.

Another specific example of an application is to improve the diagnosticaccuracy of endoscopic ultrasound (EUS) guided fine needle aspirationbiopsy (FNA) cytology for risk stratification of pancreatic cysts (i.e.,to distinguish cysts with high malignant potential (HMP) from those withlow malignant potential (LMP)). Use of endoscopic ultrasound(EUS)-guided fine-needle aspiration (FNA) cytology to detect pancreaticcancer is limited by a high false negative rate largely due to therelative rarity of frankly malignant cells. The widespread use ofradiological imaging has identified pancreatic cysts at an increasingrate. Accurate identification of cystic lesions that already harborcancer or have a high risk to become a cancer presents a uniqueopportunity for early detection. As shown in FIG. 25, there is astatistically significant increase in the intra-nuclear optical pathlength and entropy and a decrease in intra-nuclear uniformity inpancreatic cyst patients with high malignant potential (HMP) (n=11) whencompared with those in patients with low malignant potential (LMP)(n=16), with statistical significance (p<0.05).

Clinically, the SL-QPM-based nuclear analysis could be used in cases forwhich conventional cytopathology cannot make a definitive diagnosis.Since SL-QPM can be directly applied to original cytology specimenswithout any additional modification, it can rapidly be integrated intoclinical practice. This technique may also reduce the number of needlepasses required, thus potentially reducing FNA-associated complications,procedure time, and cost.

Breast Cancer

Using systems and methods of the subject innovation, progressive changein nuclear refractive index properties were found to parallel breasttumorigenesis. More importantly, the nuclear refractive index propertiesfrom histologically normal cells adjacent to the tumor can indicate thepresence of invasive breast cancer with a high accuracy (˜95%). Thus,these nuclear refractive index properties have a potential role in theearly detection and management of breast cancer.

Breast cancer is one of the most common cancers and is a second leadingcause of cancer death in women. One in eight women in the United Stateswill develop breast cancer during her lifetime. The preoperative coreneedle biopsy (CNB) is the standard of care for the initial evaluationof breast lesions in patients with abnormal imaging findings, but it haswell-known limitations. The central concern for CNB is that patients mayharbor malignant disease not captured in the limited biopsy specimen dueto sampling errors. This recognized sampling limitation can also lead tounnecessary surgery when certain pre-cancerous lesions (e.g., atypicalductal hyperplasia, papillary lesions) are found on CNB: only 10-20% ofthese cases are upgraded to cancer following surgical excision. Further,the breast conserving surgery is associated with a significantipsilateral breast tumor recurrence rate (as high as 40%) despiteapparent negative pathologic margins.

Because a complex series of stochastic genetic events via distinct anddivergent pathways progress toward invasive breast cancer, specificmolecular alterations characteristic of tumorigenesis could offer earlyand more sensitive means of detecting cancer than visual morphologicabnormalities currently used. Genetic and epigenetic changes such asloss of heterozygosity, microsatellite instability, altered geneexpression, altered DNA methylation, and other genetic mutations havebeen found in histologically normal tissue adjacent to the tumor and inmalignant cells themselves. These molecular events provide biologicalplausibility for the use of molecular markers for the early detectionand subsequent monitoring of carcinogenesis in normal tissue adjacent totumor and in precancerous lesions.

Recognizing that the nanoscale structural changes must accompany suchgenomic and epigenetic alterations, an experiment examined therefractive index properties of the cell nucleus. The refractive index isa basic optical property that has been widely used to identify asubstance or to quantify the concentration or density of particularmacromolecules. Changes in refractive index are sensitive to subtlestructural changes, even at the scale of just a few molecules. Insurface plasma resonance-based biosensors, the binding of biomolecules(e.g., protein, DNA) to a substrate can be assessed by measuring therefractive index near the surface of sensor substrate; changes as smallas 0.00001 or a mass density of 10⁻⁹ grams/mm² can be detected. Due toits high sensitivity at the molecular level, refractive index in thecell nucleus could be used to detect molecular alterations duringtumorigenesis.

In an experiment, original unmodified histology specimens of breastbiopsies processed with standard clinical protocol (formalin fixed,paraffin embedded, and stained with hematoxylin and eosin) wereevaluated. The slides were reviewed by an expert pathologist who markedthe region of interest. The refractive index was determined by dividingthe measured OPL by the known physical thickness of the tissue section(i.e., 4 μm) controlled by the microtome. It was confirmed that theselected OPL of the cell nucleus was not affected by the absorptionprofile of the histology stain, with a distinct spatial frequency peakin the Fourier-transformed spectrum from that of the stain alone. Thecell nuclei were analyzed within the marked region of interest and thepathological status of all the cells (normal, uninvolved, or malignant)were confirmed by the expert pathologist.

This experiment investigated the nuclear refractive index properties incell nuclei from normal breast tissue, benign and proliferative breastlesions, tissue adjacent to breast tumors, and invasive breastcarcinoma.

The experiment used unmodified archived histology specimens fromcore-needle biopsies processed according to standard clinical protocol(i.e., formalin-fixed, paraffin-embedded, sectioned at 4 micronthickness and hematoxylin & eosin (H&E)-stained and coverslipped). Thespecimens came from a total of 136 women categorized into 5 groupsaccording to the criteria in the published literature, including 24healthy patients undergoing reduction mammoplasty; 14 patients withbenign breast lesions; 12 patients with proliferative lesions (with andwithout atypia); and 86 patients with invasive breast cancer. The tumorstaging information for the “Uninvolved” group was as follows: Stage I:16 patients; Stage II: 10 patients; Stage III: 2 patients; no staginginformation: 4 patients. The tumor staging information for the“Malignant” group was: Stage I: 26 patients; Stage II: 18 patients;Stage III: 15 patients; Stage IV: 1 patient; no staging information: 5patients. An experienced pathologist with expertise in breast pathology(Bhargava) evaluated each slide and marked cells of interest. Thenuclear refractive index properties were analyzed for at least 40-60cell nuclei per patient.

TABLE 2 Details of five categorized patient groups and characteristicsof core needle biopsy (CNB) specimens. Pathology diagnosis Most Mean AgeNumber of cells advanced (Mean ± of analyzed by pathology standardPatient group Patients SL-QPM diagnosis deviation) Healthy 24 NormalNormal 38.3 ± 10.9 Benign 14 FCC (2), FCC (2), 44.4 ± 15.9 FCCA (3),FCCA (3), FA (9) FA (9) Proliferative* 12 IDP (3), IDP (3), 48.1 ± 10.9DEH (2), DEH (2), SA (2), SA (2), ADH (2), ADH (2), ALH (3) ALH (3)Uninvolved** 32 Normal Malignant 60.4 ± 14.4 Malignant** 65 MalignantMalignant 59.7 ± 12.4 FCC: fibrocystic changes; FA: fibroadenoma; FCCA:apocrine metaplasia; IDP: intraductal papilloma; DEH: ductal epithelialhyperplasia; SA: sclerosing adenosis; ADH: atypical ductal hyperplasia;ALH: atypical lobular hyperplasia. *The “Proliferative” group wasfurther divided into two sub-groups: without atypia (including IDP, DEH,SA) and with atypia (including ADH and ALH). **The “Uninvolved” groupand “Malignant” group came from 86 invasive cancer patients, including11 patients from whom both tumor-adjacent normal cells and malignantcells were analyzed.

To provide a quantitative measure of the nuclear refractive index map,two statistical parameters were extracted: average nuclear refractiveindex

n_(nu)

over the two-dimensional refractive index map n(x, y) of the entire cellnucleus, and intra-nuclear standard deviation of refractive index σ_(n),which describes the structural heterogeneity within the cell nucleus. Toobtain the characteristic value for an individual patient, the meanvalue of

n_(nu)

and σ_(n) was obtained by taking the average value of 40-60 cell nucleiper patient.

Statistical analysis was performed using SAS statistical softwarepackage 9.1.6 (SAS Institute Inc., Cary, N.C.). The statisticalcomparison between two patient groups was obtained using student t-test,and two-sided P-values were used for all analyses. A multivariatelogistic regression using two variables—average nuclear refractive index

n_(nu)

and intra-nuclear standard deviation of refractive index σ_(n)—isdeveloped as a prediction model to calculate the receiver operatingcharacteristic (ROC) curves.

The nuclear refractive index map was the two-dimensional spatialdistribution of refractive index from the cell nucleus. Each pixel ofthe refractive index map represented the average refractive index alongthe longitudinal direction of the tissue section (i.e., around 4 μmthick). FIG. 26 shows a representative pseudo-color nuclear refractiveindex map for each of the 5 patient groups. The nuclear refractive indexmaps revealed a progressive change in the overall value (i.e., color)and spatial distribution of nuclear refractive index from normal cellsfrom the healthy patient to the patient with proliferative lesions tothe patient with malignant tumors. It is important to note that moresimilarities are observed between uninvolved (i.e., histologicallynormal cells adjacent to malignant tumor) and malignant cells from theinvasive breast cancer patients. To quantitatively characterize thesechanges in the maps, statistical analyses of the nuclear refractiveindex properties were performed.

FIG. 27 shows the results of statistical analyses of the nuclearrefractive index properties for each of the 5 patient groups. As withthe nuclear refractive index maps, the statistical averages of

n_(nu)

and σ_(n) in the 5 patient groups reveal a progressive change thatparallels stage of tumorigenesis. Normal cells from healthy patients andmalignant cells from patients with invasive cancer show the mostsignificant statistical difference (P<1E-7), while similar values ofnuclear refractive index

n_(nu)

are seen in normal patients with reduction mammoplasty and benign breastlesions. A slightly higher nuclear refractive index

n_(nu)

was observed in proliferative lesions, but

n_(nu)

in cell nuclei from patients without cancer was significantly lower thanin those from histologically normal cells adjacent to tumor and frommalignant cells (P<0.005). There was also a slight increase of nuclearrefractive index (

n_(nu)

) in proliferative lesions with atypia compared to those without. Foreach patient, the mean value of nuclear refractive index andintra-nuclear standard deviation of refractive index were calculated byaveraging approximately 40-60 cell nuclei. The error bar representsstandard error. Box-and-whisker plots show the changes in nuclearrefractive index properties in patients with proliferative lesionswithout atypia (n=7) and with atypia (n=5). It did not reach statisticalsignificance due to small sample size.

The intra-nuclear standard deviation of refractive index σ_(n) alsoshowed a statistically significant difference between normal cells fromreduction mammoplasty and normal cells adjacent to tumor (P=4.5E-10),and between normal and proliferative lesions (P=0.02). Most importantly,for those cells marked as “normal” by the expert breast pathologist,their nuclear refractive index properties exhibited a distinct andhighly statistically significant change between normal and invasivecancer patients, suggesting SL-QPM was better able to detect thepresence of malignancy than conventional pathology.

Because age can be an important confounding factor, it was important toensure that the observed changes in nuclear refractive index propertiesreflect breast tumorigenesis rather than age difference. The experimentcompared the nuclear refractive index properties for patients divided intwo age groups: 39-49 year-olds and 50-60 year-olds. To ensuresufficient statistical power, the normal, benign and proliferativegroups were combined into a noncancerous group. As shown in FIG. 28, thenuclear refractive index properties of the noncancerous group differsignificantly from those for age-matched patients in the uninvolvedgroup (histologically normal cells adjacent to tumor) and from those forage-matched patients in the malignant group (P<0.001), consistent withwhat was observed in the mixed patient groups.

Clinically, it is important to predict the early stage tumors. Thenuclear refractive index properties (

n_(nu)

and σ_(n)) in histologically normal-appearing cells from invasive cancerpatients who only had Stage I tumor (uninvolved group—Stage I) wascompared with those of normal and proliferative groups. The statisticalsignificance for both nuclear refractive index properties was confirmedbetween normal and uninvolved-Stage I groups (P<1E-6) and betweenproliferative and uninvolved (Stage I) groups (P<1E-4).

The experiment also looked for potential differences in nuclearrefractive index properties based on the common breast tumor subtypes:estrogen receptor (ER), progesterone receptor (PR), and human epidermalgrowth factor receptor 2 (HER2). Spearman's rank correlation method wasused to analyze the correlation of nuclear refractive index propertiesof cell nuclei from patients with invasive cancer to the expressionstatus of ER, PR, and HER2 (positive vs. negative). The results showedno correlation between nuclear refractive index or intra-nuclearstandard deviation of refractive index and ER, PR, and HER2 status(P>0.1).

To evaluate the potential of nuclear refractive index properties asclinically useful biomarkers for the early detection and riskstratification of breast cancer, receiver operating characteristic (ROC)curves were calculated by using two nuclear refractive index propertiesin a logistic regression model, as shown in FIG. 29. As the ROC curvesshow, the nuclear refractive index properties can distinguish normalpatients from malignant patients, with malignant cells identified with100% accuracy (area under curve [AUC]=1). For clinical applications, itis especially important to predict malignancy in histologicallynormal-appearing cells. The nuclear refractive index properties canachieve an AUC of 0.964 when distinguishing histologically normal cellsadjacent to invasive breast cancer from normal cells from healthypatients; from benign lesions (AUC=0.921); and from proliferativelesions (AUC=0.837). Further, leave-one-out cross-validation results in100% accuracy in discriminating normal and malignant groups, 94.4%accuracy in discriminating normal and uninvolved groups, 88.8% accuracyin discriminating benign and uninvolved groups and 79.4% accuracy indiscriminating proliferative and uninvolved groups. Overall, thehistologically normal-appearing cells in the setting of invasive breastcancer have distinct nuclear refractive index properties from patientswith cells in other settings (healthy, benign and proliferative), with88.7% accuracy in the leave-one-out cross-validation test.

Applying SL-QPM to unmodified archived histology specimens, it wasobserved that nuclear refractive index properties can detect breastcancer even in histologically normal-appearing cells adjacent to thetumor, as demonstrated by the high accuracy (˜95%) in distinguishingmalignant but normal-appearing cells (cancer patients) from normal cells(healthy patients). Furthermore, the nuclear refractive index propertiesshow progressive changes—from normal breast epithelial tissue, to benignlesions, to proliferative lesions (including precancerous lesions), tomalignant cells—that parallel the progression of tumorigenesis. Thus,the changes that were observed in nuclear refractive index propertiescould reflect early events during the neoplastic transformation ofbreast epithelial cells.

One unique aspect of the nuclear refractive index properties is theirnanoscale sensitivity, which allows for the ability to detect subtlechanges in nuclear structures at a sensitivity of 0.9 nm (correspondingto a refractive index change of 0.0002). Such high sensitivity iscritical in detecting subtle changes in the cell nucleus during breasttumorigenesis. For example, the nuclear refractive index differencebetween normal cells from healthy and cancer patients is only 0.003,corresponding with an optical path length difference of 12 nm (4 μmtissue section thickness). Similarly, the difference in nuclearrefractive index of histologically normal cells from healthy versuscancer patients is only 0.0023, or an optical path length difference of˜9 nm. Such subtle nanoscale and quantitative difference are not easilydetected using conventional microscopy techniques.

The fact that histologically normal cells adjacent to tumors displaydistinct nuclear refractive index properties characteristic of cancersupports the concept of “field effect” or “field cancerization” inbreast carcinogenesis. Substantial evidence supports the biologicalbasis of such an effect, including loss of heterozygosity, genemutations, genomic instability, DNA hypermethylation, and telomeraseexpression aberrations. These molecular changes could alter cellularprocesses during neoplastic transformation, such as suppression ofgrowth, repair, apoptosis, and cell cycle regulation. One or more ofthese molecular events could partially account for the changes observedin nuclear refractive index properties in the tumor-adjacenthistologically normal cells and proliferative lesions.

Nuclear refractive index properties can be adopted as novel cellularcharacteristics to evaluate for the presence of tumorigenesis. Therefractive index is a fundamental optical property, a change in whichhas been shown to be proportional to macromolecular concentration ormass density, with a higher refractive index corresponding to increaseddensity or higher macromolecular concentration. Since cancer isultimately characterized as uncontrolled cell growth, the accompanyingincrease in DNA content could contribute to the higher nuclearrefractive index. This hypothesis was tested by characterizing nuclearrefractive index in HeLa cells throughout cell cycle. Indeed, it wasfound that the nuclear refractive index is significantly higher in thosecells synchronized at G₂/M phase and that 4n DNA content was comparablewith those arrested at G₁/S phase with 2n DNA content, suggesting acorrelation between increased nuclear refractive index and increased DNAcontent during cell cycle. These results corroborate the findings of ahigher nuclear refractive index in proliferative lesions and malignantcells, which are well known to have increased cell growth. However,nuclear refractive index is not specific to DNA and could reflectchanges in nuclear density changes from any macromolecules (e.g., DNA,RNA, protein) in the cell nucleus.

The potential clinical use of these nuclear refractive index propertiesmay be in a broad base regarding the diagnosis, prognosis, and clinicaloutcome of breast cancer, such as precancerous lesion management,personalized risk assessment and breast tumor recurrence. For example, adiagnosis of atypical ductal hyperplasia on CNB specimen can be furthertriaged to identify if it has nuclear refractive index propertiessimilar to normal breast tissue or malignant tissue. A subsequentsurgery can be avoided if the nuclear refractive index properties aresimilar to normal breast tissue and excision could be limited only tocases with nuclear refractive index properties of malignant tissue. Theaddition of other nuclear refractive index or structural properties mayfurther improve the performance characteristics.

This experiment demonstrated that nuclear refractive index propertiescan detect the presence of breast cancer in histologicallynormal-appearing cells adjacent to the tumor and can quantifyprogressive changes in the cell nucleus that parallel the progression oftumorigenesis. Thus, nuclear refractive index properties represent anovel class of biomarker with high sensitivity in detecting the onset ofmalignancy based on cellular characteristics that cannot be appreciatedusing traditional histopathology. Validation of their ability toreliably detect such carcinogenic changes would support the rapidtranslation of SL-QPM to the clinic using routinely collected,unmodified histology slides. As shown by these results, evaluation ofnuclear refractive index properties can be applied in the diagnosis andmanagement of breast cancer, including more sensitive assessment ofprecancerous lesions.

Cholangiocarcinoma (CCA)

The accurate diagnosis of cholangiocarcinoma (CCA) on bile duct biopsiesis essential in managing patients with biliary strictures. The accuracyof pathological diagnosis is often challenged by the limited amount ofavailable tissue, rarity of frankly malignant cancer cells andidentification of neoplastic cells in the setting of inflammation. Thispilot study was conducted to demonstrate the feasibility ofSL-QPM-derived nuclear refractive index properties to improve thediagnostic accuracy of bile duct biopsies by detecting neoplasticchanges not identified by conventional histology.

SL-QPM was performed on bile duct biopsies obtained from eitherERCP-guided (random) or SpyGlass-directed Spybite® biopsies. Thediagnosis was confirmed by a surgical pathologist. The study analyzedthree groups of columnar epithelial cells: benign cells from 14 patientswith benign strictures, histologically benign cells from 16 patientswith CCA, and histologically malignant cells from 13 patients with CCA.Histology specimens processed with the standard clinical protocol(formalin fixed, paraffin embedded and stained with hematoxylin andeosin) were used for nuclear refractive index analysis. The refractiveindex maps from cell nuclei were shown in FIG. 30. There was a distinctdifference between cell nuclei from benign patients and those frommalignant patients. It should be noted that the nuclear refractive indexmaps showed a great similarity between uninvolved cells (i.e.,histologically normal cells) and malignant cells from patients withcholangicarcioma.

The SL-QPM-derived nanoscale nuclear refractive index of malignant cellsfrom cancer patients was significantly increased (P-value=0.003), asshown in FIG. 31. More importantly, the nuclear refractive index fromhistologically benign cells from cancer patients is distinctly differentfrom patients with benign disease (P-value=0.01) with a 69% sensitivityand 93% specificity.

The use of SL-QPM derived nuclear refractive index represents a noveltype of biomarker for detecting malignancy in histologically benignappearing cells. This optical biomarker can be incorporated into adiagnostic algorithm in which those patients not diagnosed withmalignancy would undergo SL-QPM analysis. Additional optical ormolecular markers can also be incorporated to further improve theprediction accuracy.

Quantification of Nanoscale Nuclear Refractive Index Changes During CellCycle

Cell cycle is a fundamental biological process underlying cellproliferation, division, and DNA repair within a biological system. Theinvestigation of cell cycle is crucial in understanding variousimportant biological processes in living organisms, especially cancer.Due to the significant role of cell cycle in cancer development, resultswere obtained characterizing the refractive index from cell nucleus in ahuman cancer cell line in both synchronized cell population andthroughout a full cell cycle. Experimental results discussed hereindemonstrate that the change in nuclear refractive index has a strongcorrelation with alterations in DNA content characterized by thestandard flow cytometric analysis. Therefore, the altered DNA contentand increased cell growth may be one of the possible mechanismsresponsible for the increase in nuclear refractive index of cells fromcancer patients.

Cell cycle, a process of replicating DNA and dividing a cell, representsa fundamental biological process underlying cell proliferation,division, DNA repair and apoptosis in biological systems. Investigationof cell cycle is crucial for understanding many pathophysiological orpharmacological processes, especially in cancer. Dysregulation of celldivision, leading to uncontrolled cell growth with abnormal DNA contentis a common biological feature in human cancer. Quantitativecharacterization of cell cycle is an important tool in cancer detectionand development of cancer-targeted chemotherapy.

The eukaryotic cell progresses through four distinct phases of the cellcycle during cell division, including G₁, S, G₂, and M phases. Cells inthe inactive state containing 7.14 pg of DNA enter the cell cycle in G₁phase in which the cell is prepared for DNA replication. During thesynthesis phase (S phase), chromosomes are replicated and cells increasetheir DNA content continuously from 7.14 to 14.28 pg per cell until theyreach the state with twice the original DNA content in G₁ phase. In thenext phase G₂, the cell is prepared for mitosis (M phase) during whichtwo cells are produced with half of the DNA contents as in S and G₂phase. The 2N (or 46 chromosomes) DNA content is characteristic of cellsin G₁ phase, while cells at the end of S phase and the entire G₂ phasefeature a 4N DNA content (or chromosome number of 92).

Since alteration in DNA content is one of the most prominentcharacteristics during the cell cycle, the DNA content is often used todetermine the cell cycle phases. Several techniques have been used toquantify the DNA content, such as DNA-intercalating fluorescent markersor light-absorbing stains. Using systems and methods of the subjectinnovation, experiments were conducted that focused on thecharacterization of an important optical property of cell nucleus—thenuclear refractive index during the cell cycle phases and its relationto DNA content. The refractive index can be used to identify asubstance, or quantify the concentration and density of particularmacromolecules. This parameter provides fundamental biophysicalinformation about the composition and organizational structure of cells.The refractive index from the cell nucleus on clinical histology tissuespecimens from patients with various types of cancer has been quantifiedwith SL-QPM. Experiments have found that the nuclear refractive index issignificantly increased in malignant cells from cancer patients comparedto those in normal patients or benign diseases. More importantly, it hasbeen found that such increased nuclear refractive index can also bedetected even in those cells that are labeled as “normal” or“indeterminate” by expert pathologists, but the patients weresubsequently confirmed to have cancer. Further, since SL-QPM can bedirectly applied on the clinical pathology specimens, it can be used forclinical translation. Therefore, the nuclear refractive index can beused as a biomarker for tumorigenesis, and offers an opportunity forearly cancer detection, improving the diagnostic accuracy and riskstratification.

To put the nuclear refractive index on a firmer biological basis,experiments were conducted to determine the biological mechanisms toexplain why increased refractive index in cancer patients is observed inboth histologically normal and in malignant cells. Since abnormalalterations in cell growth are considered as one of the hallmarks ofcancer, the first focus was to characterize the nuclear refractive indexin the cell cycle. In the study, the most widely used cancer cellline—HeLa cells, a type of human cervical cancer cell line—was used asthe model system. The refractive index from the cell nucleus wasquantified at different phases of the cell cycle, which were thencompared and correlated with the results from standard flow cytometricanalysis.

The cell population was first synchronized at two distinct cell cyclephases, G₁/S and G₂/M phases, using a double thymidine block (aninhibitor of DNA synthesis) followed by treatment of cells withnocodazole (a mitotic inhibitor) according to the standard method.Specifically, HeLa cells were grown in Dulbecco's Modified Eagle Medium(DMEM) supplemented with 10% fetal bovine serum (FBS) (Mediatech, Inc)and 1% penicillin-streptomycin in a 70% humidified incubator at 37° C.,5% CO₂. Cells were first blocked at G₁/S phase using a double thymidineblock with an exposure to 2 mM Thymidine (Sigma) in the growth media for17 hours, followed by an 8 hours release period, and a second round ofexposure to 2 mM thymidine for 15 hours. At the end of the secondexposure to thymidine, there was a G₁/S-phase arrested cell populationowing to an inhibition of DNA synthesis. Four hours after the cells werereleased from the block, 100 ng/ml nocodazole was added to the mediumfor 11 hours to arrest cells at G₂/M transition due to the inhibition ofcell mitosis.

To mimic the conditions of clinical histology specimens, a cell blockwas made from the cells synchronized at G₁/S phase and G₂/M phase.Briefly, cells were trypsinized and resuspended in Cytolyt® solution(Cytec), then they were concentrated by spinning cells in a centrifugetube until a cell pellet is formed. Then the HistoGel (ThermoScientific) was added to the cell pellet. After the histogel embeddedwith cell pellet solidifies, 10% formalin was added to remove the cellblock (gel button with specimen cells) from the container. Individualslides from the cell block were then prepared following the standardtissue histology processing protocol with paraffin-embedding, sectioningat 4 μm thickness, mounting on a glass slide, removal of paraffinfollowed by staining with hematoxylin and eosin, and coverslipped. Datawas acquired for approximately 150-160 cells from each sample via SL-QPMsystem, and the nuclear refractive index was extracted using Fourieranalysis, as described in detail elsewhere.

To confirm the status of distinct phases and DNA content during cellcycle, the standard flow cytometric analysis was performed.Specifically, HeLa cells grown and processed in duplicate following theaforementioned cell culture and synchronization protocols weretrypsinized and fixed in 70% ethanol. Cells were then washed in PBS,permeabilized with 0.25% Triton X-100 in PBS, and stained with 6%propidium iodide (PI) for 15 min in the presence of RNase (100 μg/mL) ina sodium citrate buffer (40 mM) and 0.1% Trition X-100 in PBS. Stainedcells were then passed through the CyAN Flow Cytometer (Beckman CoulterInc). Since PI is a DNA intercalating dye, the fluorescence intensity isdirectly proportional to the DNA content in the nucleus. Becausedifferent amounts of DNA within the cell depend on the phases of thecell cycle, the intensity distribution of PI fluorescence can be used todetermine what percentage of cells are in different phases of the cellcycle.

FIG. 32 shows the comparison of nuclear refractive index and flowcytometric analysis of cells synchronized at G₁/S and G₂/M phases.Graphs 3200 and 3210 show the flow cytometric analysis of fluorescentintensity—a surrogate marker for DNA content, and the correspondinghistogram of nuclear refractive index of HeLa cells is shown in 3220 and3230. Cells arrested at G₁/S phase have 2N DNA content due to aninhibition of DNA synthesis with excess thymidine; while those cellsarrested at G₂/M phase have 4N DNA content, having completed DNAduplication during the S phase but without undergoing a cell division,due to the disruption of the microtubules required for cell mitosis bynocodazole treatment. Evidently, the nuclear refractive indexdistribution resembles the histogram of fluorescence intensity from theflow cytometric analysis. HeLa cells arrested at G₂/M phase with 4N DNAcontent exhibit a significantly higher nuclear refractive index, asfurther confirmed by the statistical analysis (P<1E-30) shown in 3240.

To further confirm the relationship between the nuclear refractive indexand the changes in DNA content, the progression of nuclear refractiveindex was monitored during a full cell cycle. Cells that were culturedin separate tissue culture dishes were first synchronized using theaforementioned double thymidine block protocol, and then released fromthe block. Cells from different dishes were then trypsinized andresuspended in Cytolyt® solution at different time points (4, 8, 10, 15hours) following the second release from thymidine for cell blockprocessing. The nuclear refractive index was quantified, which wascompared with the flow cytometric analysis at 4, 8 10 and 15 hours afterthe release from the second thymidine treatment (arrested at G₁/Sphase), as shown in FIGS. 33 and 34. FIG. 33 shows flow cytometricanalysis of HeLa cells at 4, 8, 10 and 15 hours to monitor a full cellcycle. FIG. 34 shows statistical averages of fluorescence intensity andnuclear refractive index at different time points during a full cellcycle. There was a clear correlation between nuclear refractive indexand DNA content at different phases of the cell cycle. At 4 hoursfollowing the G₁/S transition, approximately 50% cells had progressedinto the S phase, undergoing DNA replication, as indicated by theincreased peak at 4N DNA content. Such change was clearly reflected inthe elevated average nuclear refractive index compared to that atG₁/S(P=0.001). At 8 hours, only ˜25% cells were left with 2N DNAcontent, and the majority of cells were presented with 4N DNA content,implying that most cells had completed S phase and may have progressedinto G₂ phase. The corresponding nuclear refractive index showed afurther increase (P=0.0001). At 10 hours, more cells (˜44%) returned to2N DNA content, a result of cell mitosis that split the replicated DNAcontent to half and also led to a significant reduction in nuclearrefractive index (P=0.0003). At 15 hours, when most cells (˜80%) hadcompleted the mitosis, they showed a prominent peak at 2N DNA contentand a further reduced nuclear refractive index (P=0.000005). At thistime point, the percentage of cells with 2N DNA content were similar tothose arrested in G₁/S phase, as was the nuclear refractive index(P=0.4). These results further confirm that the alteration in theaverage nuclear refractive index was associated with the amount of DNAcontent.

These results show a strong correlation between nuclear refractive indexand alterations of DNA content during the cell cycle. The refractiveindex alteration was shown to be proportional to macromolecularconcentration or mass density based on a well-established lineardependence: n=n₀+αC, where C represents the mass density, and theproportionality coefficient α (specific refraction increment) can beapproximated as 0.185 ml/grams for most biological molecules, such asnucleic acid and protein. Therefore, the nuclear refractive index isessentially a sensitive measure of nuclear mass density or concentrationcaused by the alterations in DNA content during the cell cycle. Forexample, when DNA content doubles from G₁/S to G₂/M phase, the averagerefractive index of the cell nucleus increases by 0.0014, correspondingto a nuclear mass density change of approximately 7.6 femtograms/μm³.Since cancer is characterized as uncontrolled cell growth that is oftenassociated with higher DNA content, the increased DNA content is one ofthe likely mechanisms responsible for increased nuclear refractive indexwhich was observed in histologically normal cells and malignant cellsfrom cancer patients. However, cancer or tumorigenesis involves complexbiological processes. The observed changes in nuclear structures couldalso result from other complex genetic and epigenetic events, which needto be further investigated.

In this experiment, the characterization of nuclear refractive index wasshown during the cell cycle and the results indicated that altered DNAcontent and increased cell growth may be one of the possible mechanismsresponsible for the observed differences in nuclear refractive index.Furthermore, the nuclear refractive index can serve as an alternativeparameter to characterize the distinct phases of cell cycle from simpleglass-slide-based cell and tissue specimens. However, the nuclearrefractive index should not be considered as a DNA specific biomarker.It can be used to detect cumulative nuclear density changes arising fromany macromolecule (e.g., DNA, RNA, protein) in the cell nucleus.Additionally, the nuclear refractive index can also be used as a novelbiomarker for cancer detection.

Effects of Stains

To improve image contrast—for better diagnostic visualization ofinter-cellular architecture and intra-cellular structure of biologicalsamples—staining agents are commonly used in a clinical diagnosticprotocol. In histology samples, for example, (also used in this work)hematoxylin and eosin (H&E) staining is most common. Hematoxylin is anuclear dye and, along with an oxidizing agent and mordant, stains thenucleus to a deep purplish blue. The cytoplasm on the other hand iseosinophilic and is counterstained by the alcoholic solution of eosin toan orange-pink color. The introduction of color into the histologicalsamples provides the contrast required for their categorical diagnosticevaluation by a pathologist without resorting to surgical biopsy inevery case. There are, however, numerous instances where pathologistdiagnosis is indeterminate. In such scenarios, if an early cancerdetection technique is able to integrate seamlessly with the clinicalprotocol and provide a correct determinate diagnosis, then the proposedtechnique has considerable relevance in the clinical setting. Ability towork in a clinical setting has the additional advantage of benchmarkingthe performance of the technique for pathologically determinate samplesby comparing its diagnostic results with that of the pathologist.

To further improve and validate these results, however, the effect ofH&E stain on the measured nuclear refractive index was considered.Hematoxylin, the dominant dye in H&E stain, has been documented as adifficult stain to control and standardize. To further complicatematters, hematoxylin is a nuclear dye. Consequently, the inevitableinter-sample variations in hematoxylin lead to corresponding variationsin the measured nuclear refractive index. This is because oxidized andalum-enriched hematoxylin binds with nuclei acids and nucleoproteins(e.g. histones) to produce a complex, referred to herein as H&E complex.The presence of this complex alters the structural properties of thenucleus leading to stain-induced changes in the measured nuclearrefractive index. Thus, the presence of H&E stain is the cost of workingwithin the histology-based diagnostic protocol. This is true not justfor the techniques discussed herein, but for any technique designed forclinical use.

In aspects of the subject innovation, a correction method to remove thestain-induced nuclear refractive index variations can be used. Moreover,the method is not limited to the particular technique disclosed herein,but can be adapted to other phase-based quantitative microscopy methods.The only requirement is the capability to make reflection- andtransmission-mode measurements.

In a cell nucleus, the variation in H&E stain manifests as a variationin the concentration of the H&E complex within the cell nucleus (thecell nucleus uptake). Cell nuclei with low uptake appear lightlystained, while cells with high uptake appear darkly stained. Thetechnique discussed herein is based on linearly relating this variationin cell uptake to the corresponding variation induced in nuclearrefractive index. This linear relation results from a simplemodification to the well-established empirical linear model relatingchanges in dry cell nuclear mass to the corresponding nuclear refractiveindex changes.

As discussed above, for SL-QPM, the spectroscopic interference intensitydata cube of the cell nucleus under study (I(x,y,k); k=free spacewavenumber) can be acquired by (x-direction) horizontal scanning of the(y-direction) vertical slit of the spectrograph. Specifically, at eachscanning step x, a CCD camera can record a slice of the data cubecorresponding to the y and k directions. The spectroscopic interferencedata cube can mathematically approximated by

I(x,y,k)=|E _(r)(x,y,k)|² +|E _(s)(x,y,k)|²+2|E _(r)(x,y k)∥E_(s)(x,y,k)|cos(φ(x,y,k))  (9)

where, E_(r) (x,y,k) and E_(s) (x,y,k) are the reference and sample beamelectric fields respectively, and φ(x,y, k) is the phase differencebetween them. The pixel-wise Fourier transform of I(x,y,k) along the kdirection—after removing the bias term—gives I_(F) (x, y, z′), where z′is the optical path length. The amplitude of I_(F) (x, y, z′), |I_(F)(x, y, z′)|, can be used to find the prominent peak corresponding to theOPL of interest, z_(p). The depth-resolved phase map image of the cellnucleus can then given by equation 10:

Φ_(r)(x,y)=I(x,y,z′)I _(z′=z) _(p)   (10)

This phase map captures the phase changes corresponding to subtlenuclear structural changes. The total phase, however, has to alsoaccount for absolute phase Φ_(a)(x,y)=kz_(p). Therefore, total phase canbe written as

Φ(x,y)=Φ_(a)(x,y)+Φ_(r)(x,y)  (11)

and the corresponding OPL image can be written as

opl(x,y)=Φ(x,y)/(2k)  (12)

The factor 2 in the denominator accounts for the double path length dueto the reflectance-mode configuration. The free-space wavenumber kcorresponds to λ=550 nm, the center wavelength of the source. Given theknowledge of sample thickness L, the refractive index image of the cellnucleus is finally obtained using

opl(x,y)=(n(x,y)+Δn(x,y))L  (13)

where n(x, y) is the refractive index associated with the absolutechange in the optical path length, while the intrinsic structure of thecell nucleus is captured by Δn(x,y). It is through the sensitivemeasurement of Δn(x, y) that systems and methods of the subjectinnovation are able to classify the sample under observation.

The variation induced in nuclear refractive index by variation in theH&E complex affects the measurement of Δn(x,y). Therefore, thecorrection model discussed herein modifies the observed Δn(x, y) suchthat the inter-sample nuclear refractive index variations arising due tointer-sample H&E stain variations are removed and all the samples arestandardized to the same reference. Mathematically it is described as

δn=α _(r) C,  (14)

where, δn is the change in nuclear refractive index due to nucleoproteinor nuclei acid concentration C, and α, is a constant of proportionalityreferred to as the specific refraction increment. The specificrefraction increment measures the increase in refractive index of theprotein solution for a 1% increase in protein concentration.

Equation 14 can be re-interpreted in accordance with the Beer-Lambertlaw because the presence of H&E complex results in absorption ofincident light in accordance with it. Specifically, the right hand sideof equation 14 can be divided and multiplied by the unknown, butconstant, molar absorptivity ∈ of H&E complex to get

$\begin{matrix}{{\delta \; n} = {{( \frac{\alpha_{r}}{ɛ} )( {ɛ\; C} )} = {\beta \; {\alpha.}}}} & (15)\end{matrix}$

This operation does not change equation 14, but allows it to express Cin terms of the absorption coefficient α=∈C, and a constant ofproportionality β that can be referred to as the absorptivity-modifiedspecific refraction increment. The value of α can be computed byoperating the SL-QPM system in the transmission-mode and employing theBeer-Lambert law

A=∈LC.  (16)

The absorbance A can be calculated from the incident and transmittedlight intensity, while the sample thickness L is experimentallycontrolled using microtome sectioning. The absorption coefficient canthen be given by α=A/L.

The applicability of the Beer-Lambert law assumes a uniform absorbingmaterial. In practice this is not always true because the H&E complex ispresent only where the nucleoprotein and nucleic acids are present forhematoxylin to bind with. To mitigate this problem and ensure stricterconformity to the assumption, small regions can be considered within thenucleus and the absorption coefficient can be computed for each region.This can result in an absorption coefficient image α(x,y) where eachpixel corresponds to one single nuclear region. Therefore, equation 15can be re-written as

δn(x,y)=β(x,y)α(x,y).  (17)

δn(x,y) can be referred to as the absorption dependent refractive indexcorrection factor that can be expressed asδn(x,y)=n_(o)(x,y)−n_(c)(x,y), where n_(o)(x,y)=n(x,y)+Δn(x, y) is theobserved refractive index, and n_(c)(x,y) is the refractive index aftercorrecting for stain variation. Substituting this expression in equation17 can obtain the final form of the correction model

n _(c)(x,y)=n _(o)(x,y)−β(x,y)α(x,y).  (18)

The absorption-modified specific refractive increment is a constant thatcan be determined based on techniques described herein.

The refractive index correction factor developed above corrects on thebasis of the absorption coefficient. The absorption, however, can changedue to two factors: change in the concentration of nucleoproteins andnucleic acids, or change in the hematoxylin concentration. The former isa pathological effect, while the latter is a result of limitations inthe clinical protocol. Together they give the measured Δn(x,y). Whicheffect is corrected for in Δn(x, y) depends on the experiment designedto estimate the absorption-modified specific refraction increment. Asthe goal is to remove the effect of variations in H&E stain, anexperiment was designed where the changes in the nucleoproteins andnuclei acids were kept to a minimum by considering serial sections of asmall intestine tissue from a healthy mouse, while the hematoxylin levelis varied. However, even though hematoxylin is the dominant nuclearstain and eosin is a counterstain for the cytoplasm, the potentialeffect of eosin on the nucleus cannot be discarded. Therefore, alongwith hematoxylin, varying eosin staining levels were also considered.

In this experiment, a small intestine tissue removed from a healthy(wild type) mouse sacrificed at the age of six weeks was used. The smallintestine was washed with phosphate buffered saline (PBS) beforeprocessing. A segment of PBS washed small intestine was cut andprocessed with standard histology protocol with 10% formalin fixation,paraffin embedded and cut into 15—4 micron thick—serial sections using amicrotome. The tissue sections were mounted onto separate glass slidesand deparaffinized.

The 15 slides were grouped into 3 groups of 5 slides each, and werestained with different amounts of hematoxylin and eosin that cover theplausible range of variation in regular clinical specimens.Specifically, the experiment considered three different eosin levels forthe three groups, with five different hematoxylin levels for the fiveslides within each group. The eosin levels were: no eosin, light eosinand normal eosin, while the hematoxylin levels were: very lighthematoxylin, light hematoxylin, normal hematoxylin, dark hematoxylin,and very dark hematoxylin. After this graded staining, the H&E stainedslides were coversliped using a mounting media. FIG. 35 shows thefifteen slides. The variations in hematoxylin and eosin are visuallydiscernible. The serial sectioning of the same healthy tissue sampleensured that the variation was not pathological but due to variation instain uptake.

For each of the 15 slides, reflection- and transmission-modemeasurements were taken. The reflection-mode allowed for computation ofthe observed refractive index, while the transmission-mode allowed forthe computation of the absorption coefficient. Both were calculated forthe central wavelength of 550 nm.

From equation 19 it can be seen that the observed refractive index andthe absorption coefficient are the dependent and independent variablesrespectively, while the corrected refractive index and theabsorption-modified refraction increment are the model parameters. As iswell known, however, cells vary in shape and size. Consequently,computing a constant pixel-wise β image to correct the observed nuclearrefractive index image of different cells with different morphologiesmay not be desired. Therefore, equation 19 can be simplified bycorrecting for the average nuclear refractive index. This is areasonable simplification as in most cases the average nuclearrefractive index of a cell is of interest. However, it should be notedthat this simplification is not a limitation of the method, but was doneto simplify the calculations shown herein. The simplification results in

n ₀(x,y)

=

n _(c)(x,y)

+{circumflex over (β)}

α(x,y)

  (19)

where

denotes the averaging. Therefore, a solution could be obtained byestimating the model parameters {circumflex over (β)} and

n_(c)(x,y)

from 15 observations. This was an over-determined problem, and usingsimple linear regression {circumflex over (β)} and (n_(c) (x,y)) weredetermined to be 4.3221326E-08 and 1.539922 respectively. FIG. 36 showsthe observed refractive index plotted as a function of the absorptioncoefficient, along with the regression fit. For a significance level of0.05, the p-value for the model parameters {circumflex over (β)} and(n_(c)(x, y)) respectively were 0.0223 and 2.088E-27.

Based on the above, an absorption dependent correction method can beapplied: measure the average absorption coefficient of a given histologysample and multiply it with the model parameter {circumflex over (β)}.This product is the correction that accounts for variation in stain.Subtracting this absorption dependent correction factor from theobserved nuclear refractive index gives the corrected nuclear refractiveindex.

To study the efficacy of the linear correction model, the method wasfirst validated using a set of test slides obtained in a manner similarto those in the above experiment. Specifically, the test histologyslides were obtained from serially sectioning a wild-mousesmall-intestine tissue sample and staining the slides with unknownvariations in H&E stain. The corresponding measured refractive indiceswere plotted as a function of the absorption coefficients. Thefluctuations in the refractive index were clearly visible. The successof the model is shown by the extent to which, based on the modelparameter {circumflex over (β)} estimated above, these variations areaccounted for. The computed correction factors, when applied to theobserved refractive indices, gave the corrected refractive indices. Oncethe variations have been removed, and the nuclear refractive index wasconstant for varying absorptions. The model was also applied to two moresets of data: mouse model and breast cancer.

Animal models of human diseases are extensively used for basic researchin medicine and biology to study the nature of these diseases. Theexperiment considered the APC^(Min) mouse model that represents thehuman condition of familial adenomatous polyposis coli (APC) gene thatundergoes a germ-line mutation leading to a truncation in the APCprotein and spontaneous development of intestinal adenoma. The studyconsidered 60 cells from each of the 3 sets of 3 aged-match mice: 6 weekwild-type mice to mimic the healthy condition, and 6 week and 4.5 monthAPC^(min) mice to mimic two stages of carcinogenesis/dysplasia. The H&Estain histology slides from small intestine epithelial tissue of themice were obtained following the protocol described above. Due toclinical limitations, there are variations in staining. FIG. 37 visuallydepicts variations in stains in Min mice on the top row, and breastcancer on the bottom row. The corresponding nuclear refractive indexvariations are shown in FIG. 38A. From a biological perspective theprogression of dysplasia should increase nuclear refractive index due toan increase in nuclear heterogeneity. This pathological trend is notvisible in 3800. After applying the correction, however, thepathological trend was clearly visible in 3810 at a significance levelof 0.05.

As the next example, breast cancer was considered. Breast cancer is thesecond leading cause of death in women in United States with anestimated one in eight women predicted to develop it in her lifetime.The preoperative core needle biopsy (CNB) is the standard of care forthe initial evaluation of breast lesions in patients with abnormalimaging findings, but it suffers from sampling errors due to samplingthe area surrounding the lesion and not the lesion itself. In such ascenario, SL-QPM has shown great potential in identifying dysplasia fromthe surrounding uninvolved region. Here, the focus was on studying stainvariations in three histopathological breast cancer classes: normal (10patients), benign (10 patients) and malignant (10 patients). Normalrefers to cells from healthy patients, benign refers to healthy cellsfrom patients with carcinoma, and malignant refers to cells frommalignant tumor.

As in the animal model, clinical staining protocols have variations inH&E staining leading to corresponding variations in nuclear refractiveindex. This scenario is shown in FIG. 37. It can be seen that thesamples from the three classes are stained to different degrees. Thecorresponding fluctuations in the nuclear refractive index are shown inFIG. 38B. These fluctuations result in an unexpected large difference inthe refractive index between normal and benign cells. It is unexpectedbecause both cells being healthy, they would be expected both to havesimilar nuclear refractive index. Furthermore, the average nuclearheterogeneity of malignant cells captured by the nuclear refractiveindex was unexpectedly less than that of benign cells. Thesediscrepancies do not follow the expected pathological trend, as shown in3820. After correction, however, it can be seen in 3830 that thedifference between the nuclear refractive indices of normal and benigncells is not statistically significant at a significance level of 0.05,corroborating the truth that both are healthy cells. Moreover, thedifference between their nuclear refractive indices and that of themalignant cells was statistically significant with p-values of less than0.05. Thus it can be seen that the above-described method for correctingvariations in stain does indeed remove the effect of stain whileretaining the pathological changes.

In another experiment discussed herein, further results were obtainedregarding the effects of staining. FIG. 39 shows results indicating thatthe selected optical path length of interest is not affected by theabsorption profile of the Diff-Quik staining solution. Fouriertransformed spectrum |F(OPL)| of the original backscattering spectrumI(x, y, k) from the Diff-Quik staining solution at 3900 and a cellnucleus stained with Diff-Quik stains at 3910. The prominent peak at thelow-spatial-frequency component of |F(OPL)| came from the absorptionprofile of the Diff-Quik staining solution. Additionally, the |F(OPL)|from the Diff-Quik-stained cell nucleus has a distinctly different peak,corresponding to the optical path length of the cell nucleus (asindicated by the black arrow).

Depth-Resolved Nanoscale Structural Changes in Regulated CellProliferation and Chromatin Decondensation

A further study involved depth-resolved spatial-domain low-coherencequantitative phase microscopy (SL-QPM), an approach that utilizedcoherence gating to construct a depth-resolved structural feature vectorquantifying sub-resolution axial structural changes at different opticaldepths within the sample. As discussed below, this feature vector wasindependent of sample thickness variation, and identified depth-resolvedthree-dimensional nanoscale structural changes in clinically preparedsamples. Numerical simulations and experimental validation discussedbelow demonstrated the feasibility of the approach. Experiments werealso performed using unstained cells to investigate the nanoscalestructural changes in regulated cell proliferation through cell cycleand chromatin decondensation induced by histone acetylation.

The ability to identify structural changes in cells and tissue,especially at the nanometer scale during disease processes (e.g., celldifferentiation, proliferation, gene expression, malignanttransformation) has important implications in biomedical research andclinical diagnosis. Cellular nanoscale structural properties havereceived increasing attention as promising markers for pre-cancerous andcancerous cells. Recent studies suggest that nanoscale structuralchanges occur early in carcinogenesis and precede microscopicallydetectable cytological abnormalities. The analysis of nanoscalestructural properties has shown great promise in early diagnosis andcancer risk assessment for different types of cancers, as discussedherein. Towards this end, interferometric techniques discussedherein—e.g., spatial-domain low-coherence quantitative phase microscopy(SL-QPM)—have been developed and show potential to detect malignancy ata higher sensitivity than conventional pathology, and to quantify cancerrisk.

SL-QPM can uses a reflection-mode common-path low-coherenceinterferometric setup equipped with a broadband light source andspectroscopic detection. It can quantify changes in the optical pathlength (OPL), with nano scale sensitivity, to capture the sub-resolutionstructural changes within the cell nucleus. The OPL can be obtained bymeasuring the phase of the Fourier-transformed spectral interferencesignal at a specific optical depth of interest. The reflection-modecommon-path setup suppresses the common-mode phase noise, while thespatial low-coherence of the light source prevents back-scattered lightfrom one location within the sample from coherently combining withback-scattered light from another displaced location, resulting in thesuppression of coherence-dependent speckle noise. A broadband lightsource can be used for improved axial resolution.

Similar interferometric phase microscopy techniques have beensuccessfully used to measure transient structural changes in nervecells, perform real-time molecular detection, and measure cellulardynamics. These techniques detect the sub-resolution change in OPL at aspecific depth of interest corresponding to a distinct physicalinterface of interest with a strong refractive-index mismatch within thesample. As with these similar techniques, the implicit coherence gatingallows the rejection of contributions to phase from locations outsidethe coherence length. Although these techniques have proven to be veryvaluable in tracking dynamic structural changes, their use in analyzingthe nanoscale structural properties of static clinical specimens atdifferent disease stages has been limited. If phase microscopy can beused to reliably analyze nanoscale cellular structural propertiesdirectly on clinically prepared human cell (cytology) or tissue(histology) samples, it could have significant implications in bothbasic understanding of structural alterations during disease processesand clinical disease diagnosis. According to the standard clinicalprotocol, cells or tissue are first fixed (e.g., using formalin), andthen embedded in paraffin to prepare the cell or tissue block. Forstructural analysis, thin sections—a few microns thick—are cut from acell or tissue block using a microtome, mounted on a microscope slide,deparaffinized and finally cover-slipped using a mounting medium. One ofthe biggest challenges in using phase microscopy for structural analysisof such clinically-prepared cell and tissue samples is decoupling thevariation in section thickness from the actual structural changes withinthe cell or tissue.

However, as described herein, SL-QPM can be used to providedepth-resolved quantification of internal structural changes within thecell nucleus with regulated cell proliferation and chromatindecondensation—directly using clinically prepared unstained cellsamples—where the structural changes associated with these biologicalprocesses can be decoupled from those due to sample thicknessvariations. Both depth-resolved quantification and removal of the effectof thickness variation can be achieved by exploiting the coherencegating implicit in the spectral-domain interferometry. The resultingthickness-independent depth-resolved structural feature vector cancapture the sub-resolution structural changes at different, but fixedsample depths inside the sample.

As discussed below, an SL-QPM optical setup was used to implement theapproach. The underlying theory is presented first, followed bynumerical simulations that show the independence of the depth-resolvedstructural feature vector from variation in sample thickness, study theeffect of noise on the feature vector, and illustrate why the approachhas the ability to capture depth-resolved structural changes. Afterthat, experimental results are presented that validate the use ofimplicit coherence gating to obtain the depth-resolved structuralfeatures that are independent of section thickness. Then results fromusing clinically prepared cell blocks show the depth-resolved nanoscalestructural alterations within the cell nucleus during the regulation ofcell proliferation through cell cycle and chromatin decondensationinduced by histone acetylation with altered chromatin density andstructure. These experimental results indicate that the depth-resolvedapproach has direct applicability in analyzing samples prepared usingstandard clinical protocol, and can provide new insights into thestructural transformation of cell nuclei during cell proliferation andchromatin decondensation.

Depth-Resolved Structural Characterization

The experimental setup of SL-QPM has been described above. In brief, acollimated broadband light (e.g., from a Xenon-arc lamp) can be focusedonto the sample by an objective (e.g., NA=0.4, as used in experimentalresults discussed herein). The sample itself forms a reflection-modelow-coherence common-path interferometry configuration. The referenceand back-scattered waves from the sample can be collected and projected(e.g., by a tube lens) onto the slit of an imaging spectrograph coupledto the CCD camera mounted on a scanning stage, or can be collected viaan AOTF, as discussed in connection with system 500, above. The temporalcoherence length of the example system used to obtain results discussedbelow was 1.225 μm and the probing depth of the system was estimated tobe 79 μm.

The sample consisted of an unstained cell section with 5 μm thicknessobtained by sectioning a paraffin-embedded cell block with a microtome,mounted on a coated glass-slide (80T/20R, although other ratios can beused in various embodiments), and then cover-slipped using mountingmedium with a refractive index of 1.50, as seen in FIG. 40, whichillustrates the reflection-mode common-path interferometry setup ofthese experiments based on a clinically prepared glass slide. The setupdimensions and the axial refractive index profile have been exaggeratedfor clarity. The coated glass-slide on the top of the sample wasapproximately a millimeter thick. However, due to the small depth offield (˜4 μm) and limited probing depth of the system, when the incidentlight was focused onto the sample, the reference wave was generated bythe reflection at the sample and the coated glass-slide interface. Thereference wave was enhanced by the reflection coating on the glassslide. As the refractive index mismatch between the sample and themounting medium was small, the sample wave consisting of backscatteredwaves from the heterogeneous components inside the scattering sample wasnot dominated by the reflection from the sample-mounting mediuminterface. The detected spectral signal can be written as in equation 1,above (reproduced here):

$\begin{matrix}{{{P(k)} = {{S(k)}\lbrack {r_{r}^{2} + {\int_{0}^{z}{{r_{s}^{2}( z^{\prime} )}{z}}} + {2{\int_{0}^{z}{{r_{s}( z^{\prime} )}r_{r}{\cos ( {2{{kn}( z^{\prime} )}z^{\prime}} )}{z^{\prime}}}}}} \rbrack}},} & (1)\end{matrix}$

where S(k) is the power spectrum of the source, r_(r) is the reflectioncoefficient of the reference wave, r_(s)(z) is the scatteringcoefficient of the sample at depth z, Z is the total sample thicknessand n(z) is the refractive index distribution along the axialz-direction and k=2π/λ is the wavenumber, with λ being the wavelength.

In the context of spectral-domain interferometry, Fercher et al. showedthat under the Born approximation and the far-field assumption, the 3Dspatial frequency corresponding to a monochromatic wave, after beingscattered from the object of interest, is given by K=(k_(s)−k_(i))/2π.For the incident wave at normal illumination and collection, theback-scattered components are restricted to the axial z-direction, andthe spatial frequency reduces to

${K = {( \frac{k}{\pi} )z}},$

where z is the unit vector in the positive z-direction in frequencyspace.

Therefore, equation 1 can be re-written as equation 20,

$\begin{matrix}{{{P(K)} = {{S( \frac{K}{2} )}\lbrack {R_{r} + R_{s} + {2{\int_{0}^{z}{{r_{s}( z^{\prime} )}r_{r}{\cos ( {4\pi \; \frac{K}{2}{n( z^{\prime} )}z^{\prime}} )}{z^{\prime}}}}}} \rbrack}},} & (20)\end{matrix}$

where R_(r)=r_(r) ² and R_(s)=∫₀ ^(z) r_(s) ² (z′)dz. The Fourierinverse of equation 20 results in equation 21:

$\begin{matrix}{{{p( z_{opl} )} = {2\Gamma*\begin{bmatrix}{{( {R_{r} + R_{s}} ){\delta (0)}} +} \\{2r_{r}{\mathcal{F}^{- 1}( {\int_{0}^{z}{{r_{s}( z^{\prime} )}{\cos ( {4\pi \; \frac{K}{2}{n( z^{\prime} )}z^{\prime}} )}{z^{\prime}}}} )}}\end{bmatrix}}},} & (21)\end{matrix}$

which is a convolution of the source correlation function F with thesuperposition of the reference wave and the backscattered sample wave.As a result, the amplitude and the phase of the Fourier-transformedsignal at any given optical depth of interest has contributions onlyfrom the back-scattered waves within the coherence length around thegiven optical depth of interest. Choma et al. showed that the phase ofthe Fourier transform of the spectral signal with respect to a fixedoptical-depth location captures the sub-resolution change in OPL at thatlocation. This sub-resolution change in OPL can be calculated usingequation 3 (reproduced here),

$\begin{matrix}{{\delta \; {p( z_{opl} )}} = {\frac{\lambda_{0}}{2\pi}{\arctan ( \frac{{Im}( {p( z_{opl} )} )}{{Re}( {p( z_{opl} )} )} )}}} & (3)\end{matrix}$

where z_(opl) is the fixed optical depth location, and Im and Re denotethe imaginary and real parts of the complex convolution p(z_(opl)),respectively. Note that the factor of 2 accounts for the double OPL dueto the reflection configuration.

For conventional spectral-domain interferometry approaches, the implicitcoherence gating has been used to extract the value of sp at a peaklocation that corresponds to a distinct physical interface of interest(due to a strong refractive index mismatch within the sample). Incontrast to this traditional approach, aspects of the subject innovationcan instead exploit the implicit coherence gating by obtaining δp atfixed optical depths within the samples that have small refractive indexgradient (no strong interfaces), thereby removing the effect of samplethickness (as discussed below). The fixed optical depth locations can bechosen such that the distance between them is at least one coherencelength of the correlation function. FIG. 41 illustrates this approach,showing depth-resolved SL-QPM via coherence gating at fixed depthlocations (shown as dots). A representative spectral interference signalfrom a given scattering object for an ideal light source with nearlyinfinite bandwidth is shown in 4100. The corresponding OPL profile,obtained by Fourier transforming this spectral interference signal,represents the true OPL profile of the scattering object, and isdepicted by the distinct stem-plot in 4110. However, due to the limitedspectral bandwidth of the light source, only a limited range of thespatial frequencies from the scattering object are captured, asvisualized through the windowing function in 4100. As a result, theFourier relation between spatial frequency and OPL manifests as aconvolution between the source correlation function (i.e., Fouriertransform of the power spectral density of the source) and the actualOPL profile of the scattering object derived from an ideal light sourcewith nearly infinite bandwidth. The resulting coherence gating implicitin this convolution is illustrated in 4110. The final OPL profile usinga light source with a limited bandwidth is shown in 4120, with theoriginal distinct peaks in the true OPL profile significantly broadened.

Note that in clinically prepared samples, there is no predominantphysical interface with a significant refractive index mismatch. As aresult, the OPL profile has contributions from the backscatteringsignals from all depths within the sample, and does not exhibit adominant peak corresponding to a specific physical interface. Therefore,for clinically prepared samples, instead of identifying structuralchanges at any specific physical interface, a more cogent strategy is toidentify structural changes at fixed optical-depth locations in such away that the source correlation functions around these optical depthscover the axial sample depth-range of interest. Towards this end, inaspects, the subject innovation can employ a technique wherein theoptical-depth locations are first fixed such that they are separated byat least one coherence length. As a result, the δp value at each ofthese locations (indicated by the dots in 4110 and 4120) captures theinternal structural change within the coherence-gated optical sectionaround each optical depth. Next, depending on the sample thickness(discussed in greater detail below), a sufficient number of opticaldepth-locations can be chosen to ensure that the entire sampledepth-range of interest is covered. The δp values from theseoptical-depth locations then form the elements of a depth-resolvedfeature vector that characterizes the sub-resolution structuralalterations within the sample. Note that due to the coherence-lengthseparation, the elements of the structural feature vector—the δpvalues—are independent of each other. A necessary condition for thedepth-resolved feature vector to be valid is that the signal strength atthe selected locations is above the noise floor. As discussed below,systems and methods of the subject innovation satisfy this condition.

Effect of Sample Thickness

The actual sample thickness for a specified value is controlled by theprecision of the microtome, a tool commonly used to section the sampleinto thin slices of specified thickness for clinical cell or tissuespecimens. Due to imperfect precision in microtome sectioning, somevariation in the thickness of the sectioned sample is inevitable, asshown in FIG. 42, illustrating a representative thickness profile of asample section measured using Dektak profilometer, which has aresolution of 5 Å. If the location corresponding to the physicalinterface at the sample and mounting medium (within the OPL profile) isused, the measured δp at this interface will significantly depend on thevariation in sample thickness. However, if a fixed optical depthlocation inside the sample is chosen such that the variation in sectionthickness of the sample is outside the coherence length around thischosen location, then δp at this fixed optical depth location, and allthe other preceding locations, will not be affected by the variations insample thickness. Using ten samples, it was empirically determined thatthe maximum deviation in sample thickness from the chosen sectionthickness of 5 μm was less than 500 nm, as visually exemplified in FIG.42.

With this experimentally determined bound on sample thickness variation,numerical simulations were performed to illustrate the independence ofthe depth-resolved structural feature vector from sample thickness. Asshown in FIG. 43, illustrating a representative axial refractive indexprofile of the scattering sample, a one-dimensional (1D) axialrefractive index profile n(z) was constructed with 5 μm sample thicknesswith a step-size of 25 nm. This profile was generated using a Gaussianrandom field (GRF). Each value of n(z) was a Gaussian random variablewith mean n₀=

n(z)

and standard deviation

Δn

=√{square root over (

[n(z)−n₀]²

)}. The angular brackets denote the ensemble expectation. The Gaussianfunction was used as the two-point correlation function:

${{C_{n}(z)} = {\exp( \frac{- z^{2}}{( \frac{l_{c}}{2} )^{2}} )}},$

where I_(c) is the spatial correlation length of refractive indexrepresenting the length scale over which the spatial correlationdecreases to a negligible level. The model parameters were chosen to beconsistent with the specifications of the experimental condition.Specifically, the average refractive index of the fixed tissue sectionno was assumed to be 1.53 (the dehydrated cells and tissue were reportedto have a refractive index of 1.50 to 1.55), with the standard deviation

Δn

and the correlation length I_(c) of the spatial variation of refractiveindex being 0.002 and 50 nm, respectively. This 1D profile has beenpreviously used to model the refractive index profile of biologicalsamples. Following the common-mode reflection configuration of FIG. 40,the reflection from the sample and the glass-slide interface acts as thereference wave. The refractive index of the glass-slide was assumed tobe 1.515. Collimated light from a broadband source (498 nm-625 nm) wasnormally incident on the modeled scattering object, and thespectral-domain interference signal resulting from the superposition ofthe reference and the back-scattered waves was collected. Theback-scattered waves from the sample were generated by modeling theaxial refractive index profile as a layered media, and by using thewave-transfer matrix and scattering matrix formalism to describe thereflection from within the sample.

For the broadband light source considered, and using the relationshipbetween spatial frequency, wave-vector, and wavelength explained above,the spatial-frequency bandwidth was 0.816 1p/μm with the resultingcoherence length being 1.225 μm. Therefore, the fixed optical-depthlocations of 1.5 μm, 3 μm, 4.5 μm, and 6 μm were used, which, for a meanrefractive index of 1.53, corresponded to the physical-depth locationsof 0.98 μm, 1.96 μm, 2.94 μm, and 3.92 μm respectively. The averagephysical thickness of the scattering sample was 5 μm, corresponding tothe mean optical thickness of 7.65 μm. A variation in sample thicknessof ±500 nm translated into the change in optical thickness of ±765 nm.Consequently, the optical thickness of the sample varied between 6.8850μm and 8.4150 μm. Due to the coherence gating, the sub-resolutionstructural change in OPL, δp, at the optical depth location of 6 μmcarried the structural information of the scattering sample from thedepth range of 6±0.6125 μm, which lay well outside the variation inoptical thickness of the sample (6.8850−8.4150 μm). Therefore, it wasexpected that this variation would not affect the δp values at the fixedoptical-depth locations of 1.5 μm, 3 μm, 4.5 μm, and 6 μm. Thisexpectation was confirmed by the simulation results. FIG. 44,illustrating variation in δp as a function of variation in samplethickness, shows that δp values remain stable—with maximum deviation of2 nm—for the four optical-depth locations as the sample-sectionthickness changes from 4.5 μm to 5.5 μm.

Effect of Noise

The use of depth-resolved feature-vector to characterize structuralchange requires that the δp values for the four optical-depth locationsare not affected by noise, that is, there is no variation in δp valuesover repeated measurements of the same sample. This requirement isessential to ensure consistent and repeatable performance withoutadversely affecting the system sensitivity. Toward this end,simulation-based stability performance of feature-vector measurements asa function of SNR for shot-noise limited detection is presented. Forthis type of detection, the noise photons per detector are,

$\begin{matrix}{N_{noise} = \sqrt{\frac{{\eta \; P_{ref}\tau}\;}{hv}\frac{2}{N}}} & (22)\end{matrix}$

where, η is the quantum efficiency of the detector, τ is acquisitiontime, hv is the photon quantum energy, and N is the number of detectors.The signal photons per detector are

$\begin{matrix}{N_{signal} = {\frac{2\eta \sqrt{{P_{sig}P} - {ref}}\tau}{hv}\frac{2}{N}}} & (23)\end{matrix}$

Accounting for the fact that Fourier transform results in the coherentintegration of the signal and the incoherent integration of noise, thesystem signal-to-noise ratio (SNR) is

$\begin{matrix}{{SNR} = {10\log_{10}\frac{4\eta \; P_{sig}\tau}{hv}\frac{2}{N}{dB}}} & (24)\end{matrix}$

For the simulation a fixed refractive index profile was considered thatwas used to generate the δp values at the four optical-depth locationsfor changing SNR. The simulation was performed using the method outlinedabove. Shot noise, which follows Poisson statistics, was simulatedthrough a Gaussian process with white spectrum, whose mean is zero, andstandard deviation is proportional to √{square root over (P_(ref))}.Under experimental conditions, with sufficiently large number of photonscollected by the detector, it was reasonable to simulate a Poissondistribution through a Gaussian distribution. FIG. 45—illustrating thesub-resolution in change in OPL (δp) as a function of SNR for the samerefractive index profile at fixed optical-depth locations of 1.5 μm, 3μm, 4.5 μm and 6 μm—shows the δp values at the four optical-depthlocations for changing SNR. As can be seen, for low SNR all four δpvalues fluctuate wildly. However, as SNR increased the values convergedto a stable value, with variation in δp of less than ±0.1 nm for SNRgreater than 22 dB. FIG. 45 also indicates the SNR of 31 dB for thesystem, which is well within the stable region.

Apart from the system SNR, the presence of a distinct interface (strongrefractive index mismatch) along the refractive index profile has thepotential to adversely affect the δp values at different depth locationsdespite the coherence gating. The coherence gating was implementedimplicitly by performing a Fourier transform. The resulting correlationfunction that served as the coherence gate did not taper-off smoothly,but instead had side lobes (even after using smoothing windows such asthe Hanning window). Consequently, the energy from a strong reflectedwave can leak into the correlation functions at different depths,thereby affecting the δp values at those depths. FIG. 46 illustratesthis scenario, showing the δp value at the optical depth location of 1.5μm for changing refractive index mismatch that occurred at the opticaldepth location of 6.5 μm. The mismatch is quantified as a multiple (m)of the standard deviation

Δn

of the refractive index profile. As can be seen, for a large mismatch ofmore than three times

Δn

, the energy from the strong refractive index mismatch leaks into thecorrelation function at 1.5 μm, affecting its δp value. Therefore, theapproach is only applicable to samples whose refractive index profiledoes not have very strong refractive index mismatch.

Contribution of Structural Characteristics to δp

The depth-resolved δp values capture the sub-resolution change in OPL atthe specified optical-depth locations. This sub-resolution change in OPLquantifies the structural change within the coherence-gated opticalsection around each of the fixed optical-depth locations. To exemplifythis concept, numerical simulations were performed using the GRF modelpresented above to investigate the effect of two statistical parametersthat characterize the structural properties of the sample: the averagemagnitude of the local mean refractive index δn, and the local spatialcorrelation length of refractive index profile l_(c). The formercharacterizes the change in local density of macromolecules, while thelatter is a statistical measure of the spatial scale of therefractive-index variation (e.g., the presence of larger macromoleculesincreases l_(c)). Using these two parameters, the conditions thatindividually isolate the effects of δn and l_(c) on the measuredsub-resolution structural change were simulated. FIG. 47 shows theeffect of local change in mean refractive index of the axial refractiveindex profile that was generated from a GRF model with a fixedcorrelation length. As shown in 4700, the mean refractive index withinthe region delineated by the rectangular-box—physical depth range of 2.5μm to 3.5 μm approximately corresponding to the optical depth range of3.9 μm to 5.3 μm—was varied from 0 to 0.01.

Image 4710 shows that this change in local mean refractive index wascaptured by δp values at the fixed optical depth location of 4.5 μm.Next, keeping the local mean refractive index constant, and changing thelocal spatial correlation length of the axial refractive index profilewithin the same region as the previous example, it was observed that δpalso captured the change in l_(c). This is shown in FIG. 48,illustrating the relationship between δp and the profile correlationlength δl (nm), where the change in l_(c) was again detected at the 4.5μm optical-depth location. Combining the above two results, it wasobserved that to a first approximation δp ∝δl_(c). Image 4800 shows themean refractive index within the region delineated by therectangular-box, and image 4810 shows the change in profile correlationlength, l_(c).

Two scenarios are presented in FIG. 49—showing the depth-resolvedsub-resolution change in OPL (δp)—to illustrate the feasibility of thedepth-resolved feature vector to capture sub-resolution nanoscalestructural changes at different sample depths. Image 4900 shows thefirst example where a significant change in mean refractive index occursin the sample-section ranging from 0.5 μm to 1.5 μm, and 4910 shows anexample where the mean refractive index primarily changes within therange of 2.5 μm to 3.5 μm. For the first scenario, four hundredrefractive index profiles were generated using the GRF model with thesame δn and l_(c) to represent the complexity present in biologicalsamples. Two hundred of these profiles (exemplified by the bluerefractive index profile in 4900) were considered as is, while for theremaining two hundred profiles an average offset of 0.005 was added toδn within the sample-section ranging from 0.5 μm to 1.5 μm (redrefractive index profile in 4900). The same steps were followed torealize the second scenario, except that in that scenario, as shown in4910, the average offset was added to the sample-section ranging from2.5 μm to 3.5 μm. Images 4920 and 4930 show the corresponding calculatedδp values for the two scenarios at the four fixed optical-depthlocations of 1.5 μm, 3 μm, 4.5 μm, and 6 μm. For each refractive indexprofile, the δp values at the specified locations resulted in adepth-resolved structural feature vector. As can be seen in 4920 and4930, the change in mean refractive index δn within the sample depthrange of 0.5 μm to 1.5 μm and depth range of 2.5 μm to 3.5 μm wascaptured at the corresponding optical depth location of 1.5 μm and 4.5μm, respectively. Thus, due to the depth-resolved nature of the measuredδp values, the structural feature-vector components were well-separatedat the specified fixed optical depths where the structural changesoccurred. For the rest of the optical depths with the same local meanrefractive index 6 n, the structural feature-vector componentsoverlapped.

Experimental Results

Experimental Demonstration of Coherence Gating

The above simulations have numerically shown that implicit coherencegating can be employed to obtain a depth-resolved structural featurevector that captures the sub-resolution structural changes at different,but fixed sample depths inside the sample, independent of sectionthickness. The following experimental results validate the use ofimplicit coherence gating.

The experiments used serial tissue sections of the small intestinaltissue from a normal mouse sectioned at two different thicknesses—4 μmand 5 μm—using a microtome, placed on the coated glass slide,coverslipped with a mounting medium (n=1.50), without any staining. Asthe tissue sections were serial sections of the same tissue segment, itwas reasonable to assume that these two tissue sections had similarstructural properties, but different thickness. With the SL-QPMinstrument, the experiment analyzed the spectral interference signal foreach (x,y) pixel location from a similar tissue area—expressed as afunction of spatial frequency K. The spectral signal wasFourier-transformed to get the axial OPL profile of the sample at (x,y)location from which, using equation 3, δp(x, y, z_(olp)) at z_(opl)=1.5μm and 3 μm was then extracted. Based on the simulation results, itappeared that the implicit coherence gating should ensure that the δpvalues measured at the two optical depth locations inside the tissuesection should not be affected by the section thickness. FIG. 50illustrates the depth-resolved sub-resolution change in OPL (δp) for 4μm and 5 μm thick sections at fixed optical-depth locations within thetissue section of 1.5 μm and 3 μm. Images 5000 and 5010 show the δpvalues for 4 μm and 5 μm thick sections at optical depths of 1.5 μm and3 μm respectively. The results shown in FIG. 50 clearly show that the δpvalues at the two optical depths were independent of the sectionthickness, as predicted based on the simulation results.

System Stability

The temporal stability of the depth-resolved δp values at the fixedoptical depth locations of 1.5 μm, 3 μm, 4.5 μm, and 6 μm wereinvestigated by plotting them as a function of time. FIG. 51 illustratesthese results, showing the temporal stability of depth-resolved δpvalues as a function of time at four fixed optical depth locations. Ascan be seen these depth-resolved values were relatively stable with astandard deviation from the mean value of around 1 nm. Specifically, thestandard deviation due to temporal fluctuations in δp values at theoptical depth locations of 1.5 μm, 3 μm, 4.5 μm, and 6 μm was 0.76 nm,0.78 nm, 0.86 nm and 1.2 nm respectively. The measurements were made ona phantom consisting of a monolayer of 0.75 μm polystyrene microspheressandwiched between two 2 μm thick paraffin layers. The system SNR was 33dB.

Experiments with Biological Cells

As shown above, there are two major structural characteristics that canchange the structural feature vector: the mean refractive index δnassociated with the macromolecular density, and the spatial correlationlength of refractive index l_(c) associated with the spatial scale ofmacromolecular structures. The experiments investigated two importantcancer-related biological processes in which an immortalized cancer cellline is treated with drugs that induce changes in these two types ofstructural characteristics as proof-of-concept demonstration. In thefirst such experiment, the cell proliferation and division process ismodulated by thymidine and nocodazole during cell cycle to double theDNA content in the cell nucleus and increase the nuclear density andrefractive index (δn). The second such experiment investigated thestructural changes as a result of histone acetylation, induced byTrichostatin A (TSA), a histone deacetylase (HDAC) inhibitor. Thehistone acetylation is a well-known epigenetic change to control thegene activity. It has previously been shown using transmission electronmicroscopy and confocal microscopy that there is a subsequent localrelaxation of chromatin or chromatin decondensation, which is expectedto increase the chromatin structural correlation length (l_(c)).

Regulation of Cell Proliferation

The cell proliferation experiment analyzed the nuclear structuralchanges in HeLa cells in response to drugs that modulate the cellproliferation. Cells were first treated with double-thymidine, whichinhibited the DNA synthesis and arrested cells at G1/S phase; and thentreated with nocodazole, which prevented cell division to arrest cellsat G2/M phase, resulting in doubled DNA content in the cell nucleus andthus higher nuclear density and refractive index. Such cellsynchronization process also populates most cells at a distinct phase,thus reducing the heterogeneity in the cell population in each phase.

HeLa cells were grown in Dulbecco's Modified Eagle Medium (DMEM)supplemented with 10% fetal bovine serum (FBS) (Mediatech, Inc) and 1%penicillin-streptomycin in a 70% humidified incubator at 37° C. and 5%CO₂. Cells were treated using a protocol based on double-thymidine blockfollowed by nocodazole. As confirmed by FIG. 52, illustrating flowcytometry of HeLa cells arrested at G₁/S-phase and at G₂/M-phase, 81% ofthe cells are arrested at G₁/S phase as seen in 5200 and 70% of thecells are arrested at G₂/M phase as seen in 5210. The DNA content of thenucleus at the G₂/M phase was indeed double that of the cells at G₁/Sphase. Next a cell block was prepared to mimic the clinical samplepreparation, which was then sectioned at 5 μm. The cell-block sectionwas placed on a coated glass slide (80T/20R), de-paraffinized andcoverslipped with a mounting medium (n=1.50), without any staining.

The cell nuclei could be easily identified in the bright-field image.With the SL-QPM instrument, for each (x,y) pixel location within thecell nuclei, the spectral signal was obtained as a function of spatialfrequency K. The Fourier-transform of the spectral signal gave the axialOPL profile of the sample at the (x,y) location. FIG. 53 shows anexample of a measured spectral signal I(x,y,λ) from a cell nucleus at5300 and its Fourier transform after removing the bias term at 5310.Using equation 3, δp(x, y, z_(opl)) was then extracted from the axialOPL profile at optical depths of 1.5 μm, 3 μm, 4.5 μm and 6 μm. Theresulting depth-resolved structural feature vectors for all (x, y) pixellocations were averaged over the pixel locations within the cell nucleusto generate a mean depth-resolved structural feature vector for everycell nucleus. 90 cell nuclei were analyzed for each phase.

FIG. 54 shows the depth-resolved sub-resolution change in OPL (δp) atfour fixed optical-depth locations within the nuclei for cells at G₁/Sand G₂/M phase. The two-sided p-value is shown on each figure,calculated from the student t-test. FIG. 54 shows the comparison of δpat optical-depth locations of 1.5 μm, 3 μm, 4.5 μm and 6 μm within thecell nuclei for cells at G₁/S and G₂/M phases. Overall, a significantincrease in the average δp was seen at three out of four optical depths(1.5, 3 and 4.5 μm) within cell nuclei at G₂/M phase compared to thoseat G₁/S phase. The doubled DNA content at G₂/M phase increased thenuclear density and refractive index (δn), resulting in a higher δp. Inparticular, the depth-resolved δp shows that the most statisticallysignificant change was observed at the optical depth location of 3 μm,which approximately corresponded to the middle part of the cell nucleus.The depth-resolved average nuclear structural change between cells atG₂/M and G₁/S phases, quantified by δ(δp)=δp_(G2/M)−δp_(G1/S), ispresented in FIG. 55 for graphic visualization. It shows a heterogeneousdistribution of altered nuclear density at different optical-depthlocations, with the most significant changes occurring at the center ofthe cell nuclei (indicated by the darker areas in FIG. 55) for thissample set.

Histone Acetylation

A further experiment investigated the depth-resolved structural changein cells following histone acetylation, which were treated with TSA, ahistone deacetylase inhibitor that induces chromatin decondensation.Chromatin decondensation was anticipated to be associated with a more“loosened” chromatin conformation and an increased correlation lengthwas therefore anticipated. FIG. 56 illustrates an immunoblot ofacetyl-Histone H4 in asynchronous and synchronized G1-phase HeLa cellstreated with vehicle or TSA for 6 hours. Increased acetyl-Histone H4associated with chromatin decondensation was seen in cells treated withTSA. Asynchronous HeLa cells were treated with either 1 μM DMSO(vehicle) or 1 μM TSA in DMSO for 6 h (shown in FIG. 56, in lanes 1 and2 on the left). HeLa cells were also synchronized using thedouble-thymidine block protocol described above with the singlemodification that 1 μM DMSO or 1 μM TSA in DMSO were added in additionto the 2 mM thymidine at 30 h (shown in FIG. 56, in lanes 3 and 4 on theright). Whole cell lysates were prepared in 50 mM Tris-HCl pH 7.5, 150mM NaCl, 50 mM NaF, 1% Tween-20, 0.5% NP40, and 1× protease inhibitormixture (Roche Applied Science, Indianapolis, Ind.) on ice for 30 min.Cleared cell lysates were resolved in 4-12% Bis-Tris gels (Invitrogen,Carlsbad, Calif.) and immunoblotted with anti-acetyl-Histone H4 primaryantibody (Upstate Biotechnology, Lake Placid, N.Y.). Increased histoneacetylation was seen in both asynchronous and synchronized G₁-phasepopulations of HeLa cells treated with TSA (shown in FIG. 56, in lanes 2and 4, respectively). Cell blocks were generated using synchronizedG₁-phase populations of HeLa cells and 5 μm sections were mounted onto aglass slide without any staining as described above.

The depth-resolved structural feature vector was extracted from about70-75 cell nuclei of control and TSA-treated synchronized G1-phasepopulations of HeLa cells respectively.

FIG. 57 shows the δp values, which quantify depth-resolvedsub-resolution structural change in OPL, at four fixed optical-depthlocations of 1.5 μm, 3 μm, 4.5 μm and 6 μm within the cell nucleus forthe control and TSA-treated cells, at 5700, 5710, 5720, and 5730,respectively. The graphic visualization of the depth-resolved averagenuclear structural feature vector changes, quantified byδ(δp)=δp_(TSA)−δp_(control), is shown in FIG. 58, illustrating thedepth-resolved changes in nuclear structural feature vector distributionfor the TSA-treated cells when compared to control cells. Astatistically significant increase in the average δp was observed at twooptical depths (1.5 and 3 μm) within cell nuclei for TSA-treated cellscompared to the control cells, with the central location (one coherencelength centered around 3 μm) exhibiting the most significant difference(P=8.7E-6). The nuclear density in control and TSA-treated cells wassimilar, as both cell groups were synchronized at G₁/S phase. Assuggested by the simulation results shown in FIG. 48, the increased δpvalue in TSA-treated cells was attributed to chromatin decondensationand the increased correlation length due to TSA-induced histoneacetylation. The depth-resolved structural feature vector indeedcaptured such nanoscale chromatin conformational changes within the cellnucleus. The SL-QPM systems and methods disclosed herein, givenexperimentally reasonable SNR levels, can utilize the coherence gatingimplicit in spectral-domain interferometry to generate a depth-resolvedstructural feature vector, which can capture sub-resolution axialnanoscale structural changes at fixed sample optical depths that are atleast one coherence length apart. Notably, the depth-resolved structuralfeature vector is independent of sample thickness. This approach isapplicable to the analysis of depth-resolved nanoscale-sensitivestructural characteristics directly on the clinically prepared unstainedsample. Results presented herein have demonstrated that thedepth-resolved structural changes can provide insights into thestructural transformation during the regulation of cell proliferationthrough cell cycle and chromatin decondensation induced by histoneacetylation, and have the potential to be used as diagnostic markers forimproving cancer diagnosis or characterizing the effect ofpharmacological compounds.

FURTHER APPLICATIONS AND HISTORY

Systems and methods of the subject innovation can be used to studysmall-scale changes on a sub-cellular level, having numerous potentialapplications in medicine, biology, research, etc. These systems andmethods are sensitive to small changes in cell structure, and can beused for cancer detection, even in the early stages of tumorigenesis.Additionally, data analytic techniques described herein can providequantitative information in a variety of settings in which suchinformation was not previously available. Potential applications includethose described above, as well as others, including the following areas:pancreatic, esophageal, inflammatory bowel disease, kidney, breastcancer, cervical cancer, ulcerative colitis, improving urine cytologyfor bladder diseases, and many other scenarios and diseases, cancerousand otherwise.

Systems and methods of the subject innovation can be used to obtaininternal structural information with nanoscale resolution based onmeasuring the elastic backscattering properties. Techniques describedherein can be used to characterize of internal structures of cell nucleiof histology slide. Various statistical parameters have shown to becapable of detecting subtle changes in the cell nuclei. The subjectinnovation can be used in a variety of applications, including detectingdisease-specific cellular changes and providing diagnostic information.

In aspects, the subject innovation can be used for detection or fordiagnostics. Particularly for patients with conditions having ahigh-risk of developing tumors, the subject innovation can readily beintegrated into preventive screening. Because of the simultaneous highsensitivity and high specificity that can be achieved with systems andmethods of the subject innovation, it can be useful both fordiagnostics, including improving cytology to obtain the best data frombiopsies, and for preventive screening.

Embodiments of the subject innovation can provide better information forcells that look normal or indeterminate to conventional cytology. Thesesystems and methods can be employed in addition to traditional methods,because as explained above, both untreated samples and stained samplescan be analyzed according to techniques described herein. Incorporationof the subject innovation can also reduce the number of biopsiesnecessary to obtain a definitive diagnosis.

Although specific optical parameters are derived and applied herein, itshould be understood that other optical parameters can also be included,such as those which capture information about values of opticalproperties discussed herein, such as path length, index of refraction,amplitude, and intensity, as well as other parameters derived from theseor combinations of these, such as parameters reflecting average orglobal values across a sample, local values, variations in local orglobal values, extent to which these values vary, ratios or otherparameters that describe fluctuations in these values, scales (e.g.,distance, etc.) over which these values vary, and other parameters(e.g., means, standard deviations, entropies, etc. associated with theseparameters, etc.).

Additionally, because of the nanoscale sensitivity (on the order of 0.9nm) of the optical systems and methods described herein, the subjectinnovation can have application in a wide range of other fields whereininstrumentation or techniques with such a level of sensitivity andresolution could prove useful or advantageous. Some such applicationscould involve nanotechnology, nanoparticles such as nanospheres, thinfilms, as well as other applications in fields and industries whereinnanoscale resolution would be advantageous.

What has been described above includes examples of the innovation. Itis, of course, not possible to describe every conceivable combination ofcomponents or methodologies for purposes of describing the subjectinnovation, but one of ordinary skill in the art may recognize that manyfurther combinations and permutations of the innovation are possible.Accordingly, the innovation is intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A spatial-domain low-coherence quantitative phasemicroscopy apparatus, comprising: a light source that produces whitelight; an acousto-optical tuned filter (AOTF) that scans wavelengths totune the white light; optics that project the tuned light onto a sample,wherein the sample scatters at least a portion of the tuned light back;a camera that records the scattered portion of the tuned light, whereinthe camera records based at least in part on the wavelengths scanned bythe AOTF; and a data analysis component that analyzes the data recordedby the camera.
 2. The apparatus of claim 1, wherein the data analysiscomponent generate a depth-resolved structural feature vector to capturesub-resolution axial nanoscale structural changes in the sample.
 3. Theapparatus of claim 1, wherein the sample is covered by a glass slide,wherein one face of the glass slide is covered by a reflection-enhancingcoating.
 4. The apparatus of claim 1, further comprising a transmissionmode.
 5. The apparatus of claim 1, wherein the camera records a matrix,wherein the matrix comprises an x-axis corresponding to a wavelength ofthe recorded light and a y-axis corresponding to the spatial position.6. The apparatus of claim 5, wherein the data analysis componentproduces a three-dimensional intensity cube I(x, y, k), based at leastin part on the matrix recorded by the camera and a wavenumber k.
 7. Theapparatus of claim 1, wherein the data analysis component analyses thedata for phase information to extract three dimensional information. 8.The apparatus of claim 1, wherein the data analysis component determinesone or more optical parameters associated with the sample, wherein theone or more optical parameters are based at least in part on an opticalpathlength difference map of the sample.
 9. The apparatus of claim 1,wherein the sample contains one or more stains, and wherein the dataanalysis component determines a correction to apply to the recorded datato account for the one or more stains.
 10. A spatial-domainlow-coherence quantitative phase microscopy method, comprising:producing white light from a light source; tuning the white light withan acousto-optical tuned filter (AOTF); projecting the tuned light ontoa sample, wherein the sample scatters at least a portion of the tunedlight back; recording the scattered portion of the tuned light with acamera; and measuring the recorded scattered portion.
 11. The method ofclaim 10, further comprising: generating a depth-resolvedthree-dimensional structural feature vector based at least in part onthe measured light; and determining sub-resolution axial nanoscalestructural changes in the sample based at least in part on thedepth-resolved structural feature vector.
 12. The method of claim 10,further comprising covering the sample by a glass slide, wherein oneface of the glass slide is covered by a reflection-enhancing coating.13. The method of claim 10, further comprising recording a matrix,wherein the matrix comprises an x-axis corresponding to a wavelength ofthe measured light and a y-axis corresponding to the spatial position.14. The method of claim 13, further comprising producing athree-dimensional intensity cube I(x, y, k), based at least in part onthe matrix recorded by the camera and a wavenumber k.
 15. The method ofclaim 10, further comprising determining one or more optical parametersassociated with the sample, wherein the one or more optical parametersare based at least in part on an optical pathlength map of the sample.16. The method of claim 10, further comprising determining a correctionto apply to the recorded data to account for one or more stains presentin the sample.
 17. A spatial-domain low-coherence quantitative phasemicroscopy apparatus, comprising: means for producing white light from alight source; means for tuning the wavelength of white light; means forprojecting the tuned light onto a sample, wherein the sample scatters atleast a portion of the light back; means for recording the scatteredportion; and means for measuring the recorded light.
 18. The apparatusof claim 17, further comprising means for generating a depth-resolvedstructural feature vector based at least in part on the measured light,wherein the means for generating determine sub-resolution axialnanoscale structural changes in the sample based at least in part on thedepth-resolved structural feature vector.
 19. The apparatus of claim 17,wherein the means for measuring further record a matrix, wherein thematrix comprises an x-axis corresponding to a wavelength of the measuredlight and a y-axis corresponding to the spatial position.
 20. Theapparatus of claim 24, further comprising means for producing athree-dimensional intensity cube I(x, y, k), based at least in part onthe recorded matrix and a wavenumber k.